A novel Episodic Associative Memory model for enhanced classification accuracy

A novel approach to Episodic Associative Memory (EAM), known as Episodic Associative Memory with a Neighborhood Effect (EAMwNE) is presented in this paper. It overcomes the representation limitations of existing episodic memory models and increases the potential for their use in practical application.

[1]  Kate Smith-Miles,et al.  On learning algorithm selection for classification , 2006, Appl. Soft Comput..

[2]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[3]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[4]  Geoffrey E. Hinton.  Connectionist Learning Procedures , 1989, Artif. Intell..

[5]  Douglas L. Hintzman,et al.  Modality tags and memory for repetitions: Locus of the spacing effect , 1973 .

[6]  Irwin P. Levin,et al.  Individual differences in dealing with incomplete information: Judging clinical competence , 1991 .

[7]  Françoise Fogelman-Soulié,et al.  Disordered Systems and Biological Organization , 1986, NATO ASI Series.

[8]  Teuvo Kohonen,et al.  Correlation Matrix Memories , 1972, IEEE Transactions on Computers.

[9]  R. Shiffrin,et al.  A model for recognition memory: REM—retrieving effectively from memory , 1997, Psychonomic bulletin & review.

[10]  R. O’Reilly,et al.  Opinion TRENDS in Cognitive Sciences Vol.6 No.12 December 2002 , 2022 .

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

[12]  James L. McClelland.  Explorations In Parallel Distributed Processing , 1988 .

[13]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[14]  James A. Anderson.  Cognitive Capabilities of a Parallel System , 1986 .

[15]  D. L. Hintzman.  Repetition and Memory1 , 1976 .

[16]  Philip S. Yu,et al.  On demand classification of data streams , 2004, KDD.

[17]  J. Stephen Judd,et al.  On the complexity of loading shallow neural networks , 1988, J. Complex..

[18]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[19]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[20]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[21]  Douglas L. Hintzman,et al.  Repetition and memory: Evidence for a multiple-trace hypothesis. , 1971 .

[22]  Risto Miikkulainen,et al.  Convergence-Zone Episodic Memory: Analysis and Simulations , 1997, Neural Networks.

[23]  B B Murdock,et al.  TODAM2: a model for the storage and retrieval of item, associative, and serial-order information. , 1993, Psychological review.

[24]  D. J. Newman,et al.  UCI Repository of Machine Learning Database , 1998 .

[25]  James L. McClelland,et al.  Hippocampal conjunctive encoding, storage, and recall: Avoiding a trade‐off , 1994, Hippocampus.

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

[27]  Damminda Alahakoon,et al.  Enhancing agent autonomy and adaptive behavior by knowledge abstraction , 2004 .

[28]  Abdelsalam Helal,et al.  aZIMAs - almost Zero Infrastructure Mobile Agent System , 2002, 2002 IEEE Wireless Communications and Networking Conference Record. WCNC 2002 (Cat. No.02TH8609).

[29]  Teuvo Kohonen,et al.  Associative memory. A system-theoretical approach , 1977 .

[30]  Risto Miikkulainen,et al.  Trace feature map: a model of episodic associative memory , 2004, Biological Cybernetics.

[31]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[32]  Stephen A. Ritz,et al.  Distinctive features, categorical perception, and probability learning: some applications of a neural model , 1977 .