14.5.1 Learning, General Non-Vision Learning Issues

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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:Mar 16, 2024 at 20:36:19