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
Zimmer, L.[Lucas],
Lindauer, M.[Marius],
Hutter, F.[Frank],
Auto-Pytorch:
Multi-Fidelity MetaLearning for Efficient and Robust AutoDL,
PAMI(43), No. 9, September 2021, pp. 3079-3090.
IEEE DOI
2108
Optimization, Open area test sites, Training,
Benchmark testing, Task analysis, Pipelines,
meta-learning
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
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 .