14.5.1 Learning, General Non-Vision Learning Issues

Chapter Contents (Back)
Learning. Some papers included because they are part of larger collections, most are not computer vision related, or even computer learning. BibRef 7700

Grim, J.[Jirí], Somol, P.[Petr], Pudil, P.[Pavel],
Probabilistic neural network playing and learning Tic-Tac-Toe,
PRL(26), No. 12, September 2005, pp. 1866-1873.
Elsevier DOI 0508
BibRef

Xing, X.L.[Xiang-Lei], Wang, K.[Kejun], Lv, Z.W.[Zhuo-Wen], Zhou, Y.[Yu], Du, S.[Sidan],
Fusion of Local Manifold Learning Methods,
SPLetters(22), No. 4, April 2015, pp. 395-399.
IEEE DOI 1411
learning (artificial intelligence) BibRef

Yukawa, M., Müller, K.R.,
Why Does a Hilbertian Metric Work Efficiently in Online Learning With Kernels?,
SPLetters(23), No. 10, October 2016, pp. 1424-1428.
IEEE DOI 1610
Hilbert spaces BibRef

Cao, X., Liu, K.J.R.,
A Graphical Evolutionary Game Approach to Social Learning,
SPLetters(24), No. 6, June 2017, pp. 765-769.
IEEE DOI 1705
behavioural sciences, game theory, graph theory, benchmark centralized detector, communication complexity, game-theoretic learning method, graphical evolutionary game approach, mean field approximations, networked system, novel distributed graphical evolutionary game-theoretic learning method, private signals, social learning, Detectors, Game theory, Games, Learning systems, Sociology, Statistics, Steady-state, Distributed decision making, distributed detection, evolutionary game theory, social, learning BibRef

Mohr, F.[Felix], Wever, M.[Marcel], Tornede, A.[Alexander], Hüllermeier, E.[Eyke],
Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning,
PAMI(43), No. 9, September 2021, pp. 3055-3066.
IEEE DOI 2108
Pipelines, Runtime, Prediction algorithms, Predictive models, Machine learning, Tools, Machine learning algorithms, hierarchical runtime prediction BibRef

Liu, Z.Y.[Zheng-Ying], Pavao, A.[Adrien], Xu, Z.[Zhen], Escalera, S.[Sergio], Ferreira, F.[Fabio], Guyon, I.[Isabelle], Hong, S.[Sirui], Hutter, F.[Frank], Ji, R.R.[Rong-Rong], Junior, J.C.S.J.[Julio C. S. Jacques], Li, G.[Ge], Lindauer, M.[Marius], Luo, Z.P.[Zhi-Peng], Madadi, M.[Meysam], Nierhoff, T.[Thomas], Niu, K.[Kangning], Pan, C.[Chunguang], Stoll, D.[Danny], Treguer, S.[Sebastien], Wang, J.[Jin], Wang, P.[Peng], Wu, C.L.[Cheng-Lin], Xiong, Y.C.[You-Cheng], Zela, A.[Arbër], Zhang, Y.[Yang],
Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019,
PAMI(43), No. 9, September 2021, pp. 3108-3125.
IEEE DOI 2108
Deep learning, Task analysis, Videos, Tensors, Benchmark testing, Internet, AutoML, deep learning, meta-learning, hyperparameter optimization BibRef

Sun, T.[Tao], Shen, H.[Han], Chen, T.Y.[Tian-Yi], Li, D.S.[Dong-Sheng],
Adaptive Temporal Difference Learning With Linear Function Approximation,
PAMI(44), No. 12, December 2022, pp. 8812-8824.
IEEE DOI 2212
Markov processes, Function approximation, Convergence, Approximation algorithms, Optimization, Reinforcement learning, finite-time convergence BibRef

Huijben, I.A.M.[Iris A. M.], Kool, W.[Wouter], Paulus, M.B.[Max B.], van Sloun, R.J.G.[Ruud J. G.],
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning,
PAMI(45), No. 2, February 2023, pp. 1353-1371.
IEEE DOI 2301
Data models, Stochastic processes, Random variables, Laplace equations, Computational modeling, Standards, structured models BibRef

Kato, H.[Hiroki], Hanada, H.[Hiroyuki], Takeuchi, I.[Ichiro],
Safe RuleFit: Learning Optimal Sparse Rule Model by Meta Safe Screening,
PAMI(45), No. 2, February 2023, pp. 2330-2343.
IEEE DOI 2301
Predictive models, Random forests, Dictionaries, Analytical models, Regression tree analysis, Pattern analysis, Numerical models, combinatorial algorithms BibRef

Kuo, C.C.J.[C.C. Jay], Madni, A.M.[Azad M.],
Green learning: Introduction, examples and outlook,
JVCIR(90), 2023, pp. 103685.
Elsevier DOI 2301
Machine learning, Green learning, Trust learning, Deep learning BibRef

Ridnik, T.[Tal], Sharir, G.[Gilad], Ben-Cohen, A.[Avi], Ben-Baruch, E.[Emanuel], Noy, A.[Asaf],
ML-Decoder: Scalable and Versatile Classification Head,
WACV23(32-41)
IEEE DOI 2302
Head, Codes, Spatial databases, Decoding, Task analysis, Algorithms: Machine learning architectures, formulations, visual reasoning BibRef


Rica, E.[Elena], Álvarez, S.[Susana], Serratosa, F.[Francesc],
Tarragona Graph Database for Machine Learning Based on Graphs,
SSSPR22(302-310).
Springer DOI 2301
BibRef

Sun, J.[Jimeng], Sow, D.[Daby], Hu, J.Y.[Jian-Ying], Ebadollahi, S.[Shahram],
Localized Supervised Metric Learning on Temporal Physiological Data,
ICPR10(4149-4152).
IEEE DOI 1008
BibRef

Fausser, S.[Stefan], Schwenker, F.[Friedhelm],
Learning a Strategy with Neural Approximated Temporal-Difference Methods in English Draughts,
ICPR10(2925-2928).
IEEE DOI 1008
Game BibRef

Joko, M.[Masao], Kawahara, Y.[Yoshinobu], Yairi, T.[Takehisa],
Learning Non-linear Dynamical Systems by Alignment of Local Linear Models,
ICPR10(1084-1087).
IEEE DOI 1008
BibRef

Shamili, A.S.[Ashkan Sharifi], Bauckhage, C.[Christian], Alpcan, T.[Tansu],
Malware Detection on Mobile Devices Using Distributed Machine Learning,
ICPR10(4348-4351).
IEEE DOI 1008
BibRef

Khalili, A.H.[Amir Hossein], Wu, C.[Chen], Aghajan, H.[Hamid],
Hierarchical preference learning for light control from user feedback,
CVPR4HB10(56-62).
IEEE DOI 1006
BibRef

Masri, M.[Mazyrah], Ahmad, W.F.B.W.[Wan Fatimah Bt Wan], Nordin, S.M.[Shahrina M.], Sulaiman, S.[Suziah],
The Effect of Visual of a Courseware towards Pre-University Students' Learning in Literature,
IVIC09(822-831).
Springer DOI 0911
BibRef

Shafie, A.B.[Afza Bt], Janier, J.B.[Josefina Barnachea], Ahmad, W.F.B.W.[Wan Fatimah Bt Wan],
Visual Learning in Application of Integration,
IVIC09(832-843).
Springer DOI 0911
BibRef

Zainuddin, N.M.M.[Norziha Megat Mohammed], Zaman, H.B.[Halimah Badioze], Ahmad, A.[Azlina],
Learning Science Using AR Book: A Preliminary Study on Visual Needs of Deaf Learners,
IVIC09(844-855).
Springer DOI 0911
BibRef

Cardellach, E., Oliveras, S., Rius, A.,
GNSS Signal Interference Classified by Means of a Supervised Learning Method Applied in the Time-Frequency Domain,
CISP09(1-5).
IEEE DOI 0910
Global Navigation Satellite System. BibRef

Liu, H.Y.[Hong-Yu], Liu, X.F.[Xiao-Feng],
Adaptive Piecewise Linear Predistorter Based on PSO and Indirect Learning Architecture,
CISP09(1-3).
IEEE DOI 0910
BibRef

Ning, H.Z.[Hua-Zhong], Xu, W.[Wei], Zhou, Y.[Yue], Gong, Y.H.[Yi-Hong], Huang, T.S.[Thomas S.],
Temporal difference learning to detect unsafe system states,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Murthy, C.A., Das, M.[Mouli], De, R.K.[Rajat K.], Mukhopadhyay, S.[Subhasis],
Determination of optimal metabolic pathways through a new learning algorithm,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Learning, General Surveys, Overviews .


Last update:Apr 10, 2024 at 09:54:40