Soft-Competitive Learning Paradigms

Learning is the ability to autonomously select, update, and store relevant information in memory; and the ability to predict and create based on what has been learned.

[1]  Korris Fu-Lai Chung,et al.  Fuzzy competitive learning , 1994, Neural Networks.

[2]  Allen Gersho,et al.  Asymptotically optimal block quantization , 1979, IEEE Trans. Inf. Theory.

[3]  Ben J. A. Kröse,et al.  Learning from delayed rewards , 1995, Robotics Auton. Syst..

[4]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[5]  James M. Keller,et al.  A possibilistic approach to clustering , 1993, IEEE Trans. Fuzzy Syst..

[6]  Stanley C. Ahalt,et al.  Competitive learning algorithms for vector quantization , 1990, Neural Networks.

[7]  Naonori Ueda,et al.  A competitive and selective learning method for designing optimal vector quantizers , 1993, IEEE International Conference on Neural Networks.

[8]  Allen Gersho,et al.  Competitive learning and soft competition for vector quantizer design , 1992, IEEE Trans. Signal Process..

[9]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[10]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[12]  Erkki Oja,et al.  Rival penalized competitive learning for clustering analysis, RBF net, and curve detection , 1993, IEEE Trans. Neural Networks.

[13]  James C. Bezdek,et al.  Fuzzy Kohonen clustering networks , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[14]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[15]  Ronald J. Williams,et al.  Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.

[16]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[17]  Naonori Ueda,et al.  A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers , 1994, Neural Networks.

[18]  James M. Keller,et al.  The possibilistic C-means algorithm: insights and recommendations , 1996, IEEE Trans. Fuzzy Syst..

[19]  Peter Dayan,et al.  Technical Note: Q-Learning , 2004, Machine Learning.

[20]  Arthur L. Samuel,et al.  Some studies in machine learning using the game of checkers , 2000, IBM J. Res. Dev..

[21]  Andrew G. Barto,et al.  Reinforcement learning , 1998 .

[22]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[23]  David Zipser,et al.  Feature Discovery by Competive Learning , 1986, Cogn. Sci..