14.5.9.4 Neural Networks for Classification and Pattern Recognition

Hsu, T.C., Wang, S.D.,
The K1-Map Reduction for Pattern Classifications,
PAMI(19), No. 6, June 1997, pp. 616-622.
IEEE DOI 9708
An approach for Restricted Coulomb Energy (RCE) networks to determine the number of clusters or network centers. BibRef

Ray, K.S., Ghoshal, J.,
Neuro Fuzzy Approach to Pattern-Recognition,
NeurNet(10), No. 1, January 1997, pp. 161-182. 9702
BibRef

Ray, K.S.[Kumar S.],
Pattern Recognition Based on Fuzzy Set and Genetic Algorithm,
IJIG(14), No. 03, 2014, pp. 1450009.
DOI Link 1410
BibRef

Ornes, C., Sklansky, J.,
A Neural-Network That Visualizes What It Classifies,
PRL(18), No. 11-13, November 1997, pp. 1301-1306. 9806
BibRef

Ornes, C., Sklansky, J.,
A Visual Neural Classifier,
SMC-B(28), No. 4, August 1998, pp. 620-625.
IEEE Top Reference. 9808
BibRef

Ornes, C.[Chester], Sklansky, J.[Jack], Disher, A.[Anthony],
A Visual Neural Network that Learns Perceptual Relationships,
ICPR98(Vol I: 873-875).
IEEE DOI 9808
BibRef

Auda, G., Kamel, M.,
CMNN: Cooperative Modular Neural Networks for Pattern Recognition,
PRL(18), No. 11-13, November 1997, pp. 1391-1398. 9806
BibRef

Murino, V.,
Structured Neural Networks for Pattern Recognition,
SMC-B(28), No. 4, August 1998, pp. 553-561.
IEEE Top Reference. 9808
BibRef

Lu, Z.K., Chi, Z.R., Siu, W.C.,
Length Estimation of Digit Strings Using a Neural Network with Structure Based Features,
JEI(7), No. 1, January 1998, pp. 79-85. 9807
BibRef

Chen, C.W., Chen, L.L.,
Cellular Neural Network Architecture for Gibbs Random Field Based Image Segmentation,
JEI(7), No. 1, January 1998, pp. 45-51. 9807
BibRef

Chen, C.W., Chen, L.L., Luo, J.B.,
A Cellular Neural Network for Clustering-Based Adaptive Quantization in Subband Video Compression,
CirSysVideo(6), No. 6, December 1996, pp. 688-692.
IEEE Top Reference. 9701
BibRef

Aizenberg, I.N.,
Processing of Noisy and Small Detailed Gray Scale Images Using Cellular Neural Networks,
JEI(6), No. 3, July 1997, pp. 272-285. 9807
BibRef

Lin, C.C.[Che-Chern], El-Jaroudi, A.[Amro],
An Algorithm to Determine the Feasibilities and Weights of Two-Layer Perceptrons for Partitioning and Classification,
PR(31), No. 11, November 1998, pp. 1613-1625.
Elsevier DOI BibRef 9811

Zhou, W.,
Verification of the Nonparametric Characteristics of Backpropagation Neural Networks for Image Classification,
GeoRS(37), No. 2, March 1999, pp. 771.
IEEE Top Reference. BibRef 9903

Carozza, M.[Menita], Rampone, S.[Salvatore],
Function approximation from noisy data by an incremental RBF network,
PR(32), No. 12, December 1999, pp. 2081-2083.
Elsevier DOI BibRef 9912
And: Further results:
An incremental multivariate regression method for function approximation from noisy data,
PR(34), No. 3, March 2001, pp. 695-702.
Elsevier DOI 0101
BibRef

Lin, C.T., Lee, Y.C., Pu, H.C.,
Satellite Sensor Image Classification Using Cascaded Architecture of Neural Fuzzy Network,
GeoRS(38), No. 2, March 2000, pp. 1033-1043.
IEEE Top Reference. 0004
BibRef

Simpson, J.J., McIntire, T.J., Sienko, M.,
An Improved Hybrid Clustering Algorithm for Natural Scenes,
GeoRS(38), No. 2, March 2000, pp. 1016-1032.
IEEE Top Reference. 0004
BibRef

Rughooputh, H.C.S., Rughooputh, S.D.D.V.,
Spectral recognition using a modified Eckhorn neural network model,
IVC(18), No. 14, November 2000, pp. 1101-1103.
Elsevier DOI 0101
BibRef

Lin, J.S.[Jzau-Sheng],
Annealed chaotic neural network with nonlinear self-feedback and its application to clustering problem,
PR(34), No. 5, May 2001, pp. 1093-1104.
Elsevier DOI 0102
BibRef

Go, J.[Jinwook], Han, G.[Gunhee], Kim, H.[Hagbae], Lee, C.H.[Chul-Hee],
Multigradient: a new neural network learning algorithm for pattern classification,
GeoRS(39), No. 5, May 2001, pp. 986-993.
IEEE Top Reference. 0106
BibRef

Iyatomi, H.[Hitoshi], Hagiwara, M.[Masafumi],
Scenery image recognition and interpretation using fuzzy inference neural networks,
PR(35), No. 8, August 2002, pp. 1793-1806.
Elsevier DOI 0206
BibRef

Iyatomi, H.[Hitoshi], Hagiwara, M.[Masafumi],
Adaptive fuzzy inference neural network,
PR(37), No. 10, October 2004, pp. 2049-2057.
Elsevier DOI 0409
Initial rule, selection of important elements, identification of the network structure, parameter estimation. BibRef

Lin, J.S.[Jzau-Sheng], Liu, S.H.[Shao-Han],
Classification of multispectral images based on a fuzzy-possibilistic neural network,
SMC-C(32), No. 4, November 2002, pp. 499-506.
IEEE Top Reference. 0301
BibRef

Jiang, X.D.[Xu-Dong], Wah, A.H.K.S.[Alvin Harvey Kam Siew],
Constructing and training feed-forward neural networks for pattern classification,
PR(36), No. 4, April 2003, pp. 853-867.
Elsevier DOI 0304
BibRef

Raudys, A.,
Boosting neural network feature extraction by reduced accuracy activation functions,
PR(36), No. 6, June 2003, pp. 1343-1354.
Elsevier DOI 0304
BibRef

Venkatesh, Y.V., Raja, S.K.[S. Kumar],
On the classification of multispectral satellite images using the multilayer perceptron,
PR(36), No. 9, September 2003, pp. 2161-2175.
Elsevier DOI 0307
BibRef

Porter, R.B.[Reid B.], Harvey, N.R.[Neal R.], Perkins, S.[Simon], Theiler, J.[James], Brumby, S.P.[Steven P.], Bloch, J.J.[Jeffrey J.], Gokhale, M.[Maya], Szymanski, J.J.[John J.],
Optimizing Digital Hardware Perceptrons for Multi-Spectral Image Classification,
JMIV(19), No. 2, September 2003, pp. 133-150.
DOI Link 0308
BibRef

Park, S.B.[Soo Beom], Lee, J.W.[Jae Won], Kim, S.K.[Sang Kyoon],
Content-based image classification using a neural network,
PRL(25), No. 3, February 2004, pp. 287-300.
Elsevier DOI 0401
BibRef

Han, M.[Min], Xi, J.H.[Jian-Hui],
Efficient clustering of radial basis perceptron neural network for pattern recognition,
PR(37), No. 10, October 2004, pp. 2059-2067.
Elsevier DOI 0409
BibRef

Alaiz-Rodríguez, R.[Rocío], Guerrero-Curieses, A.[Alicia], Cid-Sueiro, J.[Jesús],
Minimax classifiers based on neural networks,
PR(38), No. 1, January 2005, pp. 29-39.
Elsevier DOI 0410
BibRef

Sanz, P.J., Marin, R., Sanchez, J.S.,
Including efficient object recognition capabilities in online robots: from a statistical to a Neural-network classifier,
SMC-C(35), No. 1, February 2005, pp. 87-96.
IEEE Abstract. 0501
BibRef

Lim, C.P.[Chee-Peng], Leong, J.H.[Jenn-Hwai], Kuan, M.M.[Mei-Ming],
A Hybrid Neural Network System for Pattern Classification Tasks with Missing Features,
PAMI(27), No. 4, April 2005, pp. 648-653.
IEEE Abstract. 0501
Classification with incomplete data. BibRef

Artyomov, E.[Evgeny], Yadid-Pecht, O.[Orly],
Modified high-order neural network for invariant pattern recognition,
PRL(26), No. 6, 1 May 2005, pp. 843-851.
Elsevier DOI 0501
BibRef

Yadid-Pecht, O.[Orly], Gur, M.,
A simple 'possibilistic' clustering neural network,
ICPR94(B:520-521).
IEEE DOI 9410
BibRef

Spratling, M.W.[Michael W.],
Learning Viewpoint Invariant Perceptual Representations from Cluttered Images,
PAMI(27), No. 5, May 2005, pp. 753-761.
IEEE Abstract. 0501
Neural network application. Learn simple patterns in all orientations. BibRef

Qin, A.K., Suganthan, P.N.,
Enhanced neural gas network for prototype-based clustering,
PR(38), No. 8, August 2005, pp. 1275-1288.
Elsevier DOI 0505
Code, Neural Networks. BibRef
Earlier:
Kernel neural gas algorithms with application to cluster analysis,
ICPR04(IV: 617-620).
IEEE DOI 0409
Code available:
WWW Link. BibRef

Chi, H.M.[Hoi-Ming], Ersoy, O.K.,
A statistical self-organizing learning system for remote sensing classification,
GeoRS(43), No. 8, August 2005, pp. 1890-1900.
IEEE DOI 0508
BibRef

Zhang, H., Huang, W., Huang, Z., Zhang, B.,
A Kernel Autoassociator Approach to Pattern Classification,
SMC-B(35), No. 3, June 2005, pp. 593-606.
IEEE DOI 0508
BibRef

Perez, C.A., Gonzalez, G.D., Medina, L.E., Galdames, F.J.,
Linear Versus Nonlinear Neural Modeling for 2-D Pattern Recognition,
SMC-A(35), No. 6, November 2005, pp. 955-964.
IEEE DOI 0510
BibRef

Cang, S., Yu, H.,
Novel probabilty neural network,
VISP(152), No. 5, October 2005, pp. 535-544.
DOI Link 0512
BibRef

Ng, W.W.Y.[Wing W.Y.], Dorado, A.[Andres], Yeung, D.S.[Daniel S.], Pedrycz, W.[Witold], and Izquierdo, E.[Ebroul],
Image classification with the use of radial basis function neural networks and the minimization of the localized generalization error,
PR(40), No. 1, January 2007, pp. 19-32.
Elsevier DOI 0611
Image classification; Radial basis functions neural networks; MPEG-7; Support vector machines; Generalization error BibRef

Chandramouli, K., Izquierdo, E.,
Image Classification using Chaotic Particle Swarm Optimization,
ICIP06(3001-3004).
IEEE DOI 0610
BibRef

del Frate, F.[Fabio], Pacifici, F.[Fabio], Schiavon, G.[Giovanni], Solimini, C.[Chiara],
Use of Neural Networks for Automatic Classification From High-Resolution Images,
GeoRS(45), No. 4, April 2007, pp. 800-809.
IEEE DOI 0704
BibRef

Meher, S.K.[Saroj K.], Uma Shankar, B., Ghosh, A.[Ashish],
Wavelet-Feature-Based Classifiers for Multispectral Remote-Sensing Images,
GeoRS(45), No. 6, June 2007, pp. 1881-1886.
IEEE DOI 0706
BibRef

Ou, G.B.[Guo-Bin], Murphey, Y.L.[Yi Lu],
Multi-class pattern classification using neural networks,
PR(40), No. 1, January 2007, pp. 4-18.
Elsevier DOI 0611
BibRef
Earlier: Add A3: Feldkamp, L.[Lee], ICPR04(IV: 585-588).
IEEE DOI 0409
Machine learning; Multi-class classification; Neural networks BibRef

Murphey, Y.L.[Yi Lu], Luo, Y.[Yun],
Feature extraction for a multiple pattern classification neural network system,
ICPR02(II: 220-223).
IEEE DOI 0211
BibRef

Shankar, B.U.[B. Uma], Meher, S.K.[Saroj K.], Ghosh, A.[Ashish], Bruzzone, L.[Lorenzo],
Remote Sensing Image Classification: A Neuro-fuzzy MCS Approach,
ICCVGIP06(128-139).
Springer DOI 0612
BibRef

Misra, B.B., Dehuri, S., Dash, P.K., Panda, G.,
A reduced and comprehensible polynomial neural network for classification,
PRL(29), No. 12, 1 September 2008, pp. 1705-1712.
Elsevier DOI 0804
Classification; Polynomial neural network; Particle swarm optimization BibRef

Ponalagusamy, R., Senthilkumar, S.,
A new fourth order embedded RKAHeM(4,4) method with error control on multilayer raster cellular neural network,
SIViP(3), No. 1, January 2009, pp. xx-yy.
Springer DOI 0902
initial value problems. BibRef

Ponalagusamy, R., Senthilkumar, S.,
A new fourth order embedded RKAHeM(4,4) method with error control on single layer/raster cellular neural network,
SIViP(3), No. 3, September 2009, pp. xx-yy.
Springer DOI 0910
BibRef

Senthilkumar, S.,
Hole-Filler Cellular Neural Network Simulation by RKGHM(5,5),
JMIV(43), No. 3, July 2012, pp. 194-205.
WWW Link. 1204
BibRef

Fontenla-Romero, O.[Oscar], Guijarro-Berdinas, B.[Bertha], Perez-Sanchez, B.[Beatriz], Alonso-Betanzos, A.[Amparo],
A new convex objective function for the supervised learning of single-layer neural networks,
PR(43), No. 5, May 2010, pp. 1984-1992.
Elsevier DOI 1003
Single-layer neural networks; Global optimum; Supervised learning method; Least squares; Convex optimization; Incremental learning BibRef

Gross, B.A.[Brooks A.], Hanna, D.M.[Darrin M.],
Artificial neural networks capable of learning spatiotemporal chemical diffusion in the cortical brain,
PR(43), No. 11, November 2010, pp. 3910-3921.
Elsevier DOI 1008
Artificial intelligence; Elman; Neural network; 3D; Chemical imaging; Brain; Neurochemistry BibRef

Yu, D.[Dong], Deng, L.[Li],
Efficient and effective algorithms for training single-hidden-layer neural networks,
PRL(33), No. 5, 1 April 2012, pp. 554-558.
Elsevier DOI 1202
Neural network; Extreme learning machine; Accelerated gradient algorithm; Weighted algorithm; MNIST BibRef

Martínez-Rego, D.[David], Fontenla-Romero, O.[Oscar], Alonso-Betanzos, A.[Amparo],
Nonlinear single layer neural network training algorithm for incremental, nonstationary and distributed learning scenarios,
PR(45), No. 12, December 2012, pp. 4536-4546.
Elsevier DOI 1208
Artificial neural networks; Incremental learning; Nonstationary learning; Distributed learning BibRef

Martinez-Rego, D.[David], Castillo, E.[Enrique], Fontenla-Romero, O.[Oscar], Alonso-Betanzos, A.[Amparo],
A Minimum Volume Covering Approach with a Set of Ellipsoids,
PAMI(35), No. 12, 2013, pp. 2997-3009.
IEEE DOI 1311
Classification BibRef

Benalcázar, M.E.[Marco E.], Brun, M.[Marcel], Ballarin, V.[Virginia], Passoni, I.[Isabel], Meschino, G.[Gustavo], Pra, L.D.[Lucía Dai],
Automatic Design of Binary W-operators Using Artificial Feed-forward Neural Networks Based on the Weighted Mean Square Error Cost Function,
CIARP12(495-502).
Springer DOI 1209
BibRef

Chen, B.[Bo], Polatkan, G.[Gungor], Sapiro, G.[Guillermo], Blei, D.[David], Dunson, D.[David], Carin, L.[Lawrence],
Deep Learning with Hierarchical Convolutional Factor Analysis,
PAMI(35), No. 8, 2013, pp. 1887-1901.
IEEE DOI 1307
Analytical models; Bayesian methods; Convolution; deep learning BibRef

Goodfellow, I.J.[Ian J.], Courville, A.[Aaron], Bengio, Y.[Yoshua],
Scaling Up Spike-and-Slab Models for Unsupervised Feature Learning,
PAMI(35), No. 8, 2013, pp. 1902-1914.
IEEE DOI 1307
Approximation methods; Neural nets BibRef

Courville, A.[Aaron], Desjardins, G., Bergstra, J., Bengio, Y.[Yoshua],
The Spike-and-Slab RBM and Extensions to Discrete and Sparse Data Distributions,
PAMI(36), No. 9, September 2014, pp. 1874-1887.
IEEE DOI 1408
Covariance matrices BibRef

Li, D., Wang, W., Ismail, F.,
Fuzzy Neural Network Technique for System State Forecasting,
Cyber(43), No. 5, 2013, pp. 1484-1494.
IEEE DOI 1309
Fuzzy neural predictors BibRef

Seyedhosseini, M.[Mojtaba], Tasdizen, T.[Tolga],
Multi-Class Multi-Scale Series Contextual Model for Image Segmentation,
IP(22), No. 11, 2013, pp. 4486-4496.
IEEE DOI 1310
electron microscopy BibRef

Seyedhosseini, M.[Mojtaba], Paiva, A.R.C.[Antonio R.C.], Tasdizen, T.[Tolga],
Image Parsing with a Three-State Series Neural Network Classifier,
ICPR10(4508-4511).
IEEE DOI 1008
BibRef

Chen, C.H.[Ching-Han], Kuo, C.M.[Chia-Ming], Yao, T.K.[Tun-Kai], Hsieh, S.H.[Sheng-Hsien],
Anisotropic Probabilistic Neural Network for Image Interpolation,
JMIV(48), No. 3, March 2014, pp. 488-498.
Springer DOI 1403
BibRef

Zuo, Z., Wang, G.,
Learning Discriminative Hierarchical Features for Object Recognition,
SPLetters(21), No. 9, Sept 2014, pp. 1159-1163.
IEEE DOI 1406
Artificial neural networks BibRef

Alvar, M.[Manuel], Rodriguez-Calvo, A.[Andrea], Sanchez-Miralles, A.[Alvaro], Arranz, A.[Alvaro],
Mixture of Merged Gaussian Algorithm using RTDENN,
MVA(25), No. 5, July 2014, pp. 1133-1144.
Springer DOI 1407
RTDENN: Real-Time Dynamic Ellipsoidal Neural Networks BibRef

Charalampous, K.[Konstantinos], Gasteratos, A.[Antonios],
A tensor-based deep learning framework,
IVC(32), No. 11, 2014, pp. 916-929.
Elsevier DOI 1410
Deep learning BibRef

Ramirez-Quintana, J.A.[Juan Alberto], Chacon-Murguia, M.I.[Mario Ignacio],
Self-adaptive SOM-CNN neural system for dynamic object detection in normal and complex scenarios,
PR(48), No. 4, 2015, pp. 1137-1149.
Elsevier DOI 1502
Video analysis BibRef

Luo, W.[Wei], Yang, J.[Jian], Xu, W.[Wei], Fu, T.[Tao],
Locality-Constrained Sparse Auto-Encoder for Image Classification,
SPLetters(22), No. 8, August 2015, pp. 1070-1073.
IEEE DOI 1502
image classification BibRef

Harikumar, R., kumar, B.V.[B. Vinoth],
Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor,
IJIST(25), No. 1, 2015, pp. 33-40.
DOI Link 1502
medical images BibRef

Liu, J.[Jing], Liu, B.Y.[Bing-Yuan], Lu, H.Q.[Han-Qing],
Detection guided deconvolutional network for hierarchical feature learning,
PR(48), No. 8, 2015, pp. 2645-2655.
Elsevier DOI 1505
Image representation BibRef

Choi, J.S.[Jae Seung],
Discrimination algorithm using voiced detection method and time-delay neural network system by 3 FFT sub-bands,
IJCVR(5), No. 2, 2015, pp. 99-111.
DOI Link 1505
BibRef

Kong, S., Jiang, Z., Yang, Q.,
Modeling Neuron Selectivity Over Simple Midlevel Features for Image Classification,
IP(24), No. 8, August 2015, pp. 2404-2414.
IEEE DOI 1505
Convolution BibRef

Zhu, S.H.[Song-Hao], Shi, Z.[Zhe], Sun, C.J.[Cheng-Jian], Shen, S.H.[Shu-Han],
Deep neural network based image annotation,
PRL(65), No. 1, 2015, pp. 103-108.
Elsevier DOI 1511
Deep learning BibRef
Earlier: A3, A1, A2, Only:
Image annotation via deep neural network,
MVA15(518-521)
IEEE DOI 1507
Computer architecture BibRef

Huang, Y.[Yan], Wang, W.[Wei], Wang, L.[Liang],
Unconstrained Multimodal Multi-Label Learning,
MultMed(17), No. 11, November 2015, pp. 1923-1935.
IEEE DOI 1511
BibRef
Earlier:
Conditional High-Order Boltzmann Machine: A Supervised Learning Model for Relation Learning,
ICCV15(4265-4273)
IEEE DOI 1602
Correlation BibRef

Huang, Y.[Yan], Wang, W.[Wei], Wang, L.A.[Li-Ang], Tan, T.N.[Tie-Niu],
Conditional High-Order Boltzmann Machines for Supervised Relation Learning,
IP(26), No. 9, September 2017, pp. 4297-4310.
IEEE DOI 1708
BibRef
Earlier:
Multi-Task Deep Neural Network for Multi-Label Learning,
ICIP13(2897-2900)
IEEE DOI 1402
Boltzmann machines, face recognition, image classification, learning (artificial intelligence), matrix decomposition, tensors, CHBM, action similarity labeling, binary classification, conditional high-order Boltzmann machines, conditional likelihood, data relation, discriminant ability enhancement, face verification, high-order multiplicative interactions, high-order parameter tensors, invariant recognition, joint likelihood, latent variables, multiple matrix factorization, pairwise input samples, relation feature classification, relation feature learning, supervised relation learning, Computational modeling, Data models, Face, Logic gates, Measurement, Supervised learning, Tensile stress, Deep learning, action similarity labeling, face verification, high-order Boltzmann machine, relation learning BibRef

An, H.[Hongsub], Shim, H.M.[Hyeon-Min], Na, S.I.[Sang-Il], Lee, S.[Sangmin],
Split and merge algorithm for deep learning and its application for additional classes,
PRL(65), No. 1, 2015, pp. 137-144.
Elsevier DOI 1511
Deep neural networks BibRef

Hile, R.[Ryan], Cova, T.J.[Thomas J.],
Exploratory Testing of an Artificial Neural Network Classification for Enhancement of the Social Vulnerability Index,
IJGI(4), No. 4, 2015, pp. 1774.
DOI Link 1511
BibRef

Sengoz, C.[Cenker], Ramanna, S.[Sheela],
Learning relational facts from the web: A tolerance rough set approach,
PRL(67, Part 2), No. 1, 2015, pp. 130-137.
Elsevier DOI 1512
Tolerance rough sets BibRef

Yang, Y.[Yang], Zhang, W.S.[Wen-Sheng], Xie, Y.[Yuan],
Image automatic annotation via multi-view deep representation,
JVCIR(33), No. 1, 2015, pp. 368-377.
Elsevier DOI 1512
Image annotation BibRef

Burian, P.[Petr], Holota, R.[Radek],
Fast image recognition based on n-tuple neural networks implemented in an FPGA,
RealTimeIP(11), No. 1, January 2016, pp. 155-166.
WWW Link. 1601
BibRef

Azizpour, H.[Hossein], Razavian, A.S.[Ali Sharif], Sullivan, J.[Josephine], Maki, A.[Atsuto], Carlsson, S.[Stefan],
Factors of Transferability for a Generic ConvNet Representation,
PAMI(38), No. 9, September 2016, pp. 1790-1802.
IEEE DOI 1609
BibRef
Earlier:
From generic to specific deep representations for visual recognition,
DeepLearn15(36-45)
IEEE DOI 1510
feature extraction BibRef

Razavian, A.S.[Ali Sharif], Azizpour, H.[Hossein], Maki, A.[Atsuto], Sullivan, J.[Josephine], Ek, C.H.[Carl Henrik], Carlsson, S.[Stefan],
Persistent Evidence of Local Image Properties in Generic ConvNets,
SCIA15(249-262).
Springer DOI 1506
BibRef

Dash, C.S.K.[C. Sanjeev Kumar], Saran, A.[Amitav], Sahoo, P.[Pulak], Dehuri, S.[Satchidananda], Cho, S.B.[Sung-Bae],
Design of self-adaptive and equilibrium differential evolution optimized radial basis function neural network classifier for imputed database,
PRL(80), No. 1, 2016, pp. 76-83.
Elsevier DOI 1609
Data mining BibRef

Passalis, N.[Nikolaos], Tefas, A.[Anastasios],
Neural Bag-of-Features learning,
PR(64), No. 1, 2017, pp. 277-294.
Elsevier DOI 1701
Encoding, Entropy, Feature extraction, Histograms, Quantization (signal), Semantics, Training. BibRef

Passalis, N.[Nikolaos], Tefas, A.[Anastasios],
Learning bag-of-embedded-words representations for textual information retrieval,
PR(81), 2018, pp. 254-267.
Elsevier DOI 1806
BibRef
Earlier:
Bag of Embedded Words learning for text retrieval,
ICPR16(2416-2421)
IEEE DOI 1705
Word embeddings, Bag-of-words, Bag-of-features, Dictionary learning, Relevance feedback, Information retrieval. BibRef

Passalis, N.[Nikolaos], Tefas, A.[Anastasios],
Learning Neural Bag-of-Features for Large-Scale Image Retrieval,
SMCS(47), No. 10, October 2017, pp. 2641-2652.
IEEE DOI 1709
Dictionaries, Encoding, Feature extraction, Histograms, Image retrieval, Image segmentation, Bag-of-features (BoFs) representation, information retrieval, neural networks, retrieval-oriented, optimization BibRef

Passalis, N.[Nikolaos], Tefas, A.[Anastasios],
Accelerating Similarity-Based Discriminant Analysis Using Class-Specific Prototypes,
ICIP18(161-165)
IEEE DOI 1809
Prototypes, Kernel, Training, Task analysis, Robustness, Optimized production technology, Similarity Embedding Framework BibRef

Ahsan, A.M.[Amin Mohamed], Mohamad, D.B.[Dzulkifli Bin],
Machine learning technique for object detection based on SURF feature,
IJCVR(7), No. 1/2, 2017, pp. 6-19.
DOI Link 1701
NN learning using SURF features BibRef

Bakhtiary, A.H.[Amir H.], Lapedriza, A.[Agata], Masip, D.[David],
Winner takes all hashing for speeding up the training of neural networks in large class problems,
PRL(93), No. 1, 2017, pp. 38-47.
Elsevier DOI 1706
Winner takes all hashing BibRef

Zhang, J.M.[Jian-Ming], Bargal, S.A.[Sarah Adel], Lin, Z.[Zhe], Brandt, J.[Jonathan], Shen, X.H.[Xiao-Hui], Sclaroff, S.[Stan],
Top-Down Neural Attention by Excitation Backprop,
IJCV(126), No. 10, October 2018, pp. 1084-1102.
Springer DOI 1809
BibRef
Earlier: A1, A3, A4, A5, A6, Only: ECCV16(IV: 543-559).
Springer DOI 1611
BibRef

Sidike, P.[Paheding], Asari, V.K.[Vijayan K.], Sagan, V.[Vasit],
Progressively Expanded Neural Network (PEN Net) for hyperspectral image classification: A new neural network paradigm for remote sensing image analysis,
PandRS(146), 2018, pp. 161-181.
Elsevier DOI 1812
Neural network, Hyperspectral image (HSI), Classification, Machine learning, Remote sensing BibRef

Rojas-Delgado, J.[Jairo], Trujillo-Rasúa, R.[Rafael], Bello, R.[Rafael],
A continuation approach for training Artificial Neural Networks with meta-heuristics,
PRL(125), 2019, pp. 373-380.
Elsevier DOI 1909
Continuation, Optimization, Neural-network BibRef

Liang, D.J.[Dao-Jun], Yang, F.[Feng], Wang, X.P.[Xiu-Ping], Ju, X.H.[Xiao-Hui],
Multi-sample inference network,
IET-CV(13), No. 6, September 2019, pp. 605-613.
DOI Link 1911
Network with multiple samples. BibRef

Rosenfeld, A.[Amir], Tsotsos, J.K.[John K.],
Incremental Learning Through Deep Adaptation,
PAMI(42), No. 3, March 2020, pp. 651-663.
IEEE DOI 2002
BibRef
And:
Intriguing Properties of Randomly Weighted Networks: Generalizing While Learning Next to Nothing,
CRV19(9-16)
IEEE DOI 1908
Task analysis, Switches, Training, Neural networks, Convolutional codes, Incremental learning, domain adaptation. Training, Neural networks, Limiting, Machine learning, Optimization, Network architecture, Kernel, Deep Learning, Optimization, Random Weights BibRef

Rosenfeld, A., Biparva, M., Tsotsos, J.K.,
Priming Neural Networks,
Cognitive18(2092-209209)
IEEE DOI 1812
Visualization, Task analysis, Object detection, Neural networks, Semantics, Image segmentation BibRef

Zhu, Z.H.[Zhi-Hui], Soudry, D.[Daniel], Eldar, Y.C.[Yonina C.], Wakin, M.B.[Michael B.],
The Global Optimization Geometry of Shallow Linear Neural Networks,
JMIV(62), No. 3, April 2020, pp. 279-292.
Springer DOI 2004
BibRef

Zhang, J.S.[Jiang-She], Hu, J.Y.[Jun-Ying], Liu, J.M.[Jun-Min],
Neural network with multiple connection weights,
PR(107), 2020, pp. 107481.
Elsevier DOI 2008
Neural network, Neurotransmitter, Interpretability, Extending dimension BibRef

Dogan, Ü.[Ürün], Deshmukh, A.A.[Aniket Anand], Machura, M.B.[Marcin Bronislaw], Igel, C.[Christian],
Label-similarity Curriculum Learning,
ECCV20(XXIX: 174-190).
Springer DOI 2010
guiding the optimization to desirable optima BibRef

Chrysos, G.G.[Grigorios G], Panagakis, Y.[Yannis],
NAPS: Non-adversarial polynomial synthesis,
PRL(140), 2020, pp. 318-324.
Elsevier DOI 2012
Polynomial neural networks, Tensor decompositions, Generative models BibRef

Chrysos, G.G.[Grigorios G.], Moschoglou, S.[Stylianos], Bouritsas, G.[Giorgos], Deng, J.K.[Jian-Kang], Panagakis, Y.[Yannis], Zafeiriou, S.P.[Stefanos P.],
Deep Polynomial Neural Networks,
PAMI(44), No. 8, August 2022, pp. 4021-4034.
IEEE DOI 2207
Tensors, Neural networks, Task analysis, Faces, Training, Matrix decomposition, Convolutional neural networks, face verification BibRef

Yu, Y.H.[Yong-Hong], Jiao, L.H.[Li-Hong], Zhou, N.N.[Ning-Ning], Zhang, L.[Li], Yin, H.Z.[Hong-Zhi],
Enhanced factorization machine via neural pairwise ranking and attention networks,
PRL(140), 2020, pp. 348-357.
Elsevier DOI 2012
Recommendation algorithm, Factorization machine, Neural networks BibRef

Wang, Z., Xiang, C., Zou, W., Xu, C.,
DMA Regularization: Enhancing Discriminability of Neural Networks by Decreasing the Minimal Angle,
SPLetters(27), 2020, pp. 2089-2093.
IEEE DOI 2012
Image Classification, Discrimination Regularization, Intra-class Compactness, Inter-class Discrepancy, Deep Learning BibRef

Li, M.[Ming], Wang, D.H.[Dian-Hui],
2-D Stochastic Configuration Networks for Image Data Analytics,
Cyber(51), No. 1, January 2021, pp. 359-372.
IEEE DOI 2012
Data models, Data analysis, Computational modeling, Analytical models, Approximation algorithms, Neural networks, 2-D stochastic configuration networks (2DSCNs) BibRef

Shi, J.[Jun], Xu, J.F.[Jian-Feng], Tasaka, K.[Kazuyuki], Chen, Z.B.[Zhi-Bo],
SASL: Saliency-Adaptive Sparsity Learning for Neural Network Acceleration,
CirSysVideo(31), No. 5, 2021, pp. 2008-2019.
IEEE DOI 2105
BibRef

Li, J.[Jia], Xiao, M.Q.[Ming-Qing], Fang, C.[Cong], Dai, Y.[Yue], Xu, C.[Chao], Lin, Z.C.[Zhou-Chen],
Training Neural Networks by Lifted Proximal Operator Machines,
PAMI(44), No. 6, June 2022, pp. 3334-3348.
IEEE DOI 2205
Training, Artificial neural networks, Linear programming, Convergence, Tuning, Standards, Patents, Neural networks, parallel implementation BibRef

Wang, S.P.[Shi-Peng], Yang, Y.[Yan], Sun, J.[Jian], Xu, Z.B.[Zong-Ben],
Variational HyperAdam: A Meta-Learning Approach to Network Training,
PAMI(44), No. 8, August 2022, pp. 4469-4484.
IEEE DOI 2207
Training, Task analysis, Optimization, Neural networks, Random variables, Training data, Estimation, Network training, variational hyperadam BibRef

Gao, T.[Tao], Bai, X.[Xiao], Wang, C.[Chen], Zhang, L.[Liang], Zheng, J.[Jin], Wang, J.[Jian],
A modified interval type-2 Takagi-Sugeno fuzzy neural network and its convergence analysis,
PR(131), 2022, pp. 108861.
Elsevier DOI 2208
IT2 fuzzy model, Fuzzy neural network, Takagi-Sugeno, Conjugate gradient, Convergence BibRef

Elyounsi, A.[Asma], Tlijani, H.[Hatem], Bouhlel, M.S.[Mohamed Salim],
Firefly Algorithm Optimized Functional Link Artificial Neural Network for ISA-Radar Image Recognition,
IJIG(22), No. 5 2022, pp. 2250044.
DOI Link 2212
BibRef

Tovias-Alanis, S.O.[Samuel Omar], Sossa, H.[Humberto], Gómez-Flores, W.[Wilfrido],
Learning smooth dendrite morphological neurons for pattern classification using linkage trees and evolutionary-based hyperparameter tuning,
PRL(172), 2023, pp. 274-281.
Elsevier DOI 2309
Dendrite morphological neurons, Spherical dendrites, Linkage trees, Pattern classification, Genetic algorithm BibRef

Groenendijk, R.[Rick], Dorst, L.[Leo], Gevers, T.[Theo],
Geometric Back-Propagation in Morphological Neural Networks,
PAMI(45), No. 11, November 2023, pp. 14045-14051.
IEEE DOI 2310
BibRef

Yu, X.H.[Xiao-Han], Mao, S.C.[Shao-Chen], Wang, L.[Lei], Lu, S.J.[Shi-Jie], Yu, K.[Kun],
Research on neural processes with multiple latent variables,
IET-IPR(17), No. 11, 2023, pp. 3323-3336.
DOI Link 2310
Combines the advantages of neural network and Gaussian Process. encoder-decoder, multiple latent variables, neural process, regression BibRef

Cai, S.[Sudong],
IIEU: Rethinking Neural Feature Activation from Decision-Making,
ICCV23(5773-5783)
IEEE DOI 2401
Instantaneous Importance Estimation Units BibRef

Tao, C.X.[Chen-Xin], Zhu, X.[Xizhou], Su, W.J.[Wei-Jie], Huang, G.[Gao], Li, B.[Bin], Zhou, J.[Jie], Qiao, Y.[Yu], Wang, X.G.[Xiao-Gang], Dai, J.F.[Ji-Feng],
Siamese Image Modeling for Self-Supervised Vision Representation Learning,
CVPR23(2132-2141)
IEEE DOI 2309
BibRef

Spallanzani, M.[Matteo], Leonardi, G.P.[Gian Paolo], Benini, L.[Luca],
Training Quantised Neural Networks with STE Variants: The Additive Noise Annealing Algorithm,
CVPR22(470-479)
IEEE DOI 2210
Training, Schedules, Annealing, Shape, Neural networks, Stochastic processes, Dynamic scheduling, Optimization methods, Efficient learning and inferences BibRef

Ricci, S.[Simone], Uricchio, T.[Tiberio], del Bimbo, A.[Alberto],
Learning Advisor Networks for Noisy Image Classification,
CIAP22(II:442-453).
Springer DOI 2205
BibRef

Li, F.R.[Fan-Rong], Li, G.[Gang], He, X.Y.[Xiang-Yu], Cheng, J.[Jian],
Dynamic Dual Gating Neural Networks,
ICCV21(5310-5319)
IEEE DOI 2203
Limiting, Computational modeling, Neural networks, Redundancy, Channel estimation, Network architecture, BibRef

Cordonnier, J.B.[Jean-Baptiste], Mahendran, A.[Aravindh], Dosovitskiy, A.[Alexey], Weissenborn, D.[Dirk], Uszkoreit, J.[Jakob], Unterthiner, T.[Thomas],
Differentiable Patch Selection for Image Recognition,
CVPR21(2351-2360)
IEEE DOI 2111
Training, Image resolution, Image recognition, Computational modeling, Neural networks, Memory management BibRef

Bungert, L.[Leon], Raab, R.[René], Roith, T.[Tim], Schwinn, L.[Leo], Tenbrinck, D.[Daniel],
Clip: Cheap Lipschitz Training of Neural Networks,
SSVM21(307-319).
Springer DOI 2106
BibRef

Limnios, S.[Stratis], Dasoulas, G.[George], Thilikos, D.M.[Dimitrios M.], Vazirgiannis, M.[Michalis],
Hcore-Init: Neural Network Initialization based on Graph Degeneracy,
ICPR21(5852-5858)
IEEE DOI 2105
Deep learning, Knowledge engineering, Image recognition, Neurons, Tools, Multilayer perceptrons, Data mining BibRef

Wang, X.L.[Xing-Lu], Li, Y.M.[Ying-Ming],
Gradient Deconfliction-Based Training For Multi-Exit Architectures,
ICIP20(1866-1870)
IEEE DOI 2011
Early exit with easy samples. Training, Computational modeling, Adaptation models, Neural networks, Task analysis, Acceleration, Deconfliction BibRef

Tran, D.T.[Dat Thanh], Gabbouj, M.[Moncef], Iosifidis, A.[Alexandros],
Subset Sampling for Progressive Neural Network Learning,
ICIP20(713-717)
IEEE DOI 2011
Training, Network topology, Neurons, Biological neural networks, Training data, Optimization, Face recognition, Subset Selection, Progressive Neural Network Learning BibRef

Yang, H., Tang, M., Wen, W., Yan, F., Hu, D., Li, A., Li, H., Chen, Y.,
Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification,
EDLCV20(2899-2908)
IEEE DOI 2008
Training, Matrix decomposition, Convolution, Computational modeling, Kernel, Load modeling, Tensile stress BibRef

Huang, C., Chen, J., Wu, J.,
Learning Sparse Neural Networks Through Mixture-Distributed Regularization,
EDLCV20(2968-2977)
IEEE DOI 2008
Logic gates, Training, Neurons, Artificial neural networks, Computational modeling, Exponential distribution BibRef

Zhou, Y.[Yi], Barnes, C.[Connelly], Lu, J.W.[Jing-Wan], Yang, J.M.[Ji-Mei], Li, H.[Hao],
On the Continuity of Rotation Representations in Neural Networks,
CVPR19(5738-5746).
IEEE DOI 2002
BibRef

Guo, Q.S.[Qiu-Shan], Yu, Z.P.[Zhi-Peng], Wu, Y.C.[Yi-Chao], Liang, D.[Ding], Qin, H.Y.[Hao-Yu], Yan, J.J.[Jun-Jie],
Dynamic Recursive Neural Network,
CVPR19(5142-5151).
IEEE DOI 2002
BibRef

Usama, M.[Muhammad], Chang, D.E.[Dong Eui],
Towards Robust Neural Networks with Lipschitz Continuity,
IWDW18(373-389).
Springer DOI 1905
BibRef

Manessi, F.[Franco], Rozza, A.[Alessandro],
Learning Combinations of Activation Functions,
ICPR18(61-66)
IEEE DOI 1812
Training, Biological neural networks, Mercury (metals), Optimization, Neurons, Computer architecture BibRef

Horii, K., Maeda, K., Ogawa, T.[Takahiro], Haseyama, M.[Miki],
A Human-Centered Neural Network Model with Discriminative Locality Preserving Canonical Correlation Analysis for Image Classification,
ICIP18(2366-2370)
IEEE DOI 1809
Visualization, Biology, Correlation, Training, Feature extraction, Neural networks, Transforms, Image classification, neural network, canonical correlation analysis BibRef

Zhao, R.W.[Rui-Wei], Li, J.G.[Jian-Guo], Chen, Y.R.[Yu-Rong], Liu, J.M.[Jia-Ming], Jiang, Y.G.[Yu-Gang], Xue, X.Y.[Xiang-Yang],
Regional Gating Neural Networks for Multi-label Image Classification,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Wang, Y.N.[Yu-Nong], Bian, H.Y.[Huan-Yu], Yu, N.H.[Neng-Hai],
Neural network with saliency based feature selection ability,
ICIP17(4502-4506)
IEEE DOI 1803
Benchmark testing, Biological neural networks, Feature extraction, Kernel, Task analysis, Visualization, visual saliency BibRef

Grzeszick, R., Sudholt, S., Fink, G.A.,
Optimistic and pessimistic neural networks for object recognition,
ICIP17(350-354)
IEEE DOI 1803
Biological neural networks, Computational modeling, Neurons, Predictive models, Task analysis, Training, Uncertainty, Output Modeling BibRef

Pavez, J.[Juan], Hakobyan, H.[Hayk], Valle, C.[Carlos], Brooks, W.[William], Kuleshov, S.[Sergey], Allende, H.[Héctor],
Neural Networks for the Reconstruction and Separation of High Energy Particles in a Preshower Calorimeter,
CIARP17(491-498).
Springer DOI 1802
BibRef

Haeffele, B.D., Vidal, R.,
Global Optimality in Neural Network Training,
CVPR17(4390-4398)
IEEE DOI 1711
Algorithm design and analysis, Biological neural networks, Loss measurement, Minimization, Neurons, Optimization, Training BibRef

Dong, X., Huang, J., Yang, Y., Yan, S.,
More is Less: A More Complicated Network with Less Inference Complexity,
CVPR17(1895-1903)
IEEE DOI 1711
Acceleration, Collaboration, Computational modeling, Convolution, Kernel, Neural networks, Tensile, stress BibRef

Kaoutar, S., Mohamed, E.,
Multi-criteria optimization of neural networks using multi-objective genetic algorithm,
ISCV17(1-4)
IEEE DOI 1710
Pareto optimisation, genetic algorithms, minimisation, multilayer perceptrons, vectors, MLPNN, NSGA II algorithm, Pareto set, absolute weights, architecture objective optimization, BibRef

Srinivas, S., Subramanya, A., Babu, R.V.,
Training Sparse Neural Networks,
ECVW17(455-462)
IEEE DOI 1709
Biological neural networks, Complexity theory, Indexes, Logic gates, Sparse matrices, Training BibRef

Meng, N., So, H.K.H., Lam, E.Y.,
Computational single-cell classification using deep learning on bright-field and phase images,
MVA17(190-193)
DOI Link 1708
Feature extraction, Imaging, Machine learning, Microprocessors, Neural networks, Training BibRef

Cui, S.Q.[Shu-Qi], Jiang, H.[Hong], Wang, Z.[Zheng], Shen, C.M.[Chao-Min],
Application of neural network based on SIFT local feature extraction in medical image classification,
ICIVC17(92-97)
IEEE DOI 1708
Biological neural networks, Feature extraction, Image classification, Medical diagnostic imaging, Neurons, BP neural network, ROI, SIFT, SVM, slide, the, window BibRef

Kampffmeyer, M.[Michael], Løkse, S.[Sigurd], Bianchi, F.M.[Filippo M.], Jenssen, R.[Robert], Livi, L.[Lorenzo],
Deep Kernelized Autoencoders,
SCIA17(I: 419-430).
Springer DOI 1706
BibRef

Nilsson, N., Ortiz-Catalan, M.,
Estimates of Classification Complexity for Myoelectric Pattern Recognition,
ICPR16(2682-2687)
IEEE DOI 1705
Artificial neural networks, Complexity theory, Correlation, Electromyography, Indexes, Pattern recognition, Silicon BibRef

Peng, K.H.[Kang-Hao], Zhang, H.[Heng],
Mutual information-based RBM neural networks,
ICPR16(2458-2463)
IEEE DOI 1705
Annealing, Entropy, Manganese, Monte Carlo methods, Mutual information, Neural networks, Training BibRef

Liu, L.[Lei],
Hierarchical learning for large multi-class network classification,
ICPR16(2307-2312)
IEEE DOI 1705
Additives, Computational modeling, Covariance matrices, Linear programming, Matrix decomposition, Optimization, Testing BibRef

Kalra, S., Sriram, A., Rahnamayan, S., Tizhoosh, H.R.,
Learning opposites using neural networks,
ICPR16(1213-1218)
IEEE DOI 1705
Approximation algorithms, Convergence, Data mining, Neural networks, Optimization, Training, Training, data BibRef

Roy, A., Todorovic, S., Latecki, L.J.,
Context-regularized learning of fully convolutional networks for scene labeling,
ICPR16(3751-3756)
IEEE DOI 1705
Context, Labeling, Layout, Semantics, Standards, Training, Training, data BibRef

Nooka, S.P., Chennupati, S., Veerabhadra, K., Sah, S., Ptucha, R.,
Adaptive hierarchical classification networks,
ICPR16(3578-3583)
IEEE DOI 1705
Adaptation models, Adaptive systems, Couplings, Feature extraction, Neural networks, Training, Convolutional Neural Network, Decomposition, Hierarchy, Image Classification, Muli-layer, Perceptron BibRef