'Competitive learning' diaporamas de présentation

Brain Code and Brain Reading

Brain Code and Brain Reading. Prof.dr. Jaap Murre University of Maastricht University of Amsterdam jaap@murre.com http://neuromod.org. What principle should it use? Storage capacity Resistance to damage Access speed Serve necessary calculations. What constraints can be identified?

923 views • 65 slides

Unsupervised Learning

Unsupervised Learning. G.Anuradha. Contents. Introduction Competitive Learning networks Kohenen self-organizing networks Learning vector quantization Hebbian learning. Introduction.

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Self Organized Map (SOM) Neural Network

Self Organized Map (SOM) Neural Network. Self Organized Map (SOM). The self-organizing map (SOM) is a method for unsupervised learning , based on a grid of artificial neurons whose weights are adapted to match input vectors in a training set.

1.58k views • 54 slides

Neural Networks ( ??????? ) BY ?????

Neural Networks ( ??????? ) BY ?????. 1) What is a neural network ?

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Competitive Networks

Competitive Networks. Outline. Hamming Network. Hamming Network. Layer 1 (Correlation). Layer 2 (Competition). Layer 2 (Competition). Competitive Transfer Function. Competitive Layer. Competitive Learning. Graphical Representation. Example. Competition. Update. Diagram. Convergence.

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74.419 Artificial Intelligence 2004 - Neural Networks -

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Introduction Hebbian learning Generalised Hebbian learning algorithm Competitive learning

Lecture 8. Artificial neural networks:. Unsupervised learning. Introduction Hebbian learning Generalised Hebbian learning algorithm Competitive learning Self-organising computational map: Kohonen network Summary. Introduction.

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Neural Networks Chapter 9

Neural Networks Chapter 9. Joost N. Kok Universiteit Leiden. Unsupervised Competitive Learning. Competitive learning Winner-take-all units Cluster/Categorize input data Feature mapping. 1. 2. 3. Unsupervised Competitive Learning. winner. output. input (n-dimensional).

561 views • 36 slides

Lecture 09 Clustering-based Learning

Lecture 09 Clustering-based Learning. Topics Basics K-Means Self-Organizing Maps Applications Discussions. Basics. Clustering Grouping a collection of objects (examples) into clusters, such that objects are most similar inside each cluster and least similar between clusters.

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Self-organizing map (SOM)

Self-organizing map (SOM). Presented by Sasinee Pruekprasert 48052112 Thatchaphol Saranurak 49050511 Tarat Diloksawatdikul 49051006 Department of Computer Engineering, Faculty of Engineering, Kasetsart University. Title. What SOM? Unsupervised learning Competitive learning Algorithm

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Self-organization and error correction

Computational Intelligence. Self-organization and error correction. Based on a course taught by Prof. Randall O'Reilly University of Colorado and Prof. W?odzis?awa Ducha Uniwersytet Miko?aja Kopernika. Janusz A. Starzyk. How should an ideal learning system look?

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3) Vector Quantization (VQ) and Learning Vector Quantization (LVQ)

3) Vector Quantization (VQ) and Learning Vector Quantization (LVQ). References M. Biehl, A. Freking, G. Reents Dynamics of on-line competitive learning Europhysics Letters 38 (1997) 73-78 M. Biehl, A. Ghosh, B. Hammer Dynamics and generalization ability of LVQ algorithms

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Projection Pursuit

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DESIGN FOR PBL

DESIGN FOR PBL. PREPARED BY: SURINA BINTI MUHAMMAD FAKHRUDDIN SUMARTINI BTE MAHADI. PXGT 6103: INSTRUCTIONAL DESIGN AND DEVELOPMENT. Problem-Based Learning (PBL). What is Problem-Based Learning?.

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Self-organizing map

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Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 1 0 SOM and Growing Cell Structures W?odzis?aw Duch Dept. of Informatics, UMK Google: W Duch. Italian olive oil. An example of SOM application:. 572 samples of olive oil were collected from 9 Italian provinces .

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Computational Intelligence: Methods and Applications

Computational Intelligence: Methods and Applications. Lecture 1 0 SOM and Growing Cell Structures W?odzis?aw Duch Dept. of Informatics, UMK Google: W Duch. Italian olive oil. An example of SOM application:. 572 samples of olive oil were collected from 9 Italian provinces .

220 views • 15 slides

Competitive Learning Neural Networks

Competitive Learning Neural Networks. UnSupervised Learning. Competitive Learning.

665 views • 14 slides

Population Based Incremental Learning

Population Based Incremental Learning. Shumeet Baluja Presented by KC Tsui. Background. Populations based search, such as GA Create a probability matrix by counting the number of 1s and 0s in each gene position Generate new population using the probability matrix

312 views • 8 slides