'Organizing map' diaporamas de présentation

LING 696B: Categorical perception, perceptual magnets and neural nets

LING 696B: Categorical perception, perceptual magnets and neural nets. From last time: quick overview of phonological acquisition. Prenatal to newborn 4-day-old French babies prefer listening to French over Russian (Mehler et al, 1988)

598 views • 52 slides

More Analysis of Gene Expression Data

563 views • 43 slides

Introduction to Cluster Analysis

Introduction to Cluster Analysis. Dr. Chaur-Chin Chen Department of Computer Science National Tsing Hua University Hsinchu 30013, Taiwan http://www.cs.nthu.edu.tw/~cchen. Cluster Analysis (Unsupervised Learning).

581 views • 42 slides

Temporal Sequence Processing using Recurrent SOM

Temporal Sequence Processing using Recurrent SOM. Advisor ? Dr. Hsu Graduate ? Ching-Lung Chen Author ? Timo Koskela Markus Varsta Jukka Heikkonen Kimmo Kaski. Outline. Motivation Objective Introduction Recurrent Self-Organizing Map

506 views • 32 slides

Temporal Sequence Processing using Recurrent SOM

Temporal Sequence Processing using Recurrent SOM. Advisor ? Dr. Hsu Graduate ? Ching-Lung Chen Author ? Timo Koskela Markus Varsta Jukka Heikkonen Kimmo Kaski. Outline. Motivation Objective Introduction Recurrent Self-Organizing Map

351 views • 32 slides

418 views • 27 slides

Adaptive Offset Subspace Self-Organizing Map with an Application to Handwritten Digit Recognition

471 views • 27 slides

Outlines

Consensus clustering based on constrained self-organizing map and improved Cop-Kmeans ensemble in intelligent decision support systems. Presenter : Chuang, Kai-Ting Authors : Yan Yang, Wei Tan, Tianrui Li, Da Ruan 2012, KBS. Outlines. Motivation Objectives Methodology

271 views • 26 slides

Fast algorithm and implementation of dissimilarity self-organizing maps

Fast algorithm and implementation of dissimilarity self-organizing maps. Reporter: Ming-Jui Kuo D9515007. Outline. Introduction The DSOM Simulation Results. Introduction. A drawback in standard SOM.

403 views • 25 slides

Encoding Robotic Sensor States for Q-Learning using the Self-Organizing Map

Gabriel J. Ferrer Department of Computer Science Hendrix College. Encoding Robotic Sensor States for Q-Learning using the Self-Organizing Map. Outline. Statement of Problem Q-Learning Self-Organizing Maps Experiments Discussion. Statement of Problem. Goal Make robots do what we want

301 views • 24 slides

Joint seismic attributes visualization using Self-Organizing Maps

298 views • 21 slides

Self-organizing maps applied to information retrieval of dissertations and theses from BDTD-UFPE

Self-organizing maps applied to information retrieval of dissertations and theses from BDTD-UFPE. Bruno Pinheiro bfp@cin.ufpe.br Renato Correa renato.correa@ufpe.br. Guide. Information Retrieval Systems (IRS) IRS + SOM Related Works Document Collection System Architecture Methodology

281 views • 20 slides

351 views • 17 slides

Dr. Gari D. Clifford, University Lecturer & Associate Director,

Information Driven Healthcare: Data Visualization & Classification Lecture 2: Visualization Centre for Doctoral Training in Healthcare Innovation. Dr. Gari D. Clifford, University Lecturer & Associate Director, Centre for Doctoral Training in Healthcare Innovation,

253 views • 16 slides

Self-organizing map

353 views • 16 slides

Self-Organizing Maps

Self-Organizing Maps. Corby Ziesman March 21, 2007. Overview. A Self-Organizing Map (SOM) is a way to represent higher dimensional data in an usually 2-D or 3-D manner, such that similar data is grouped together. It runs unsupervised and performs the grouping on its own.

217 views • 15 slides

255 views • 14 slides

Presenter : JHOU, YU-LIANG Authors : Teuvo Kohonen 2013,NN

Essentials of the self-organizing map. Presenter : JHOU, YU-LIANG Authors : Teuvo Kohonen 2013,NN. Outlines. Motivation Objectives Methodology Application Conclusions Comments. Motivation.

270 views • 11 slides

Self-Organizing Maps for Land Cover Classification

Presentation for ECE539 Jason Mielens ( mielens@wisc.edu ) 12/12/08. Self-Organizing Maps for Land Cover Classification. Land Cover Overview. Map of surface type Open Water Grassland Bare / Rocky Forest Urban / Developed LandSat Satellite Captures reflectance in 7 wavelengths

181 views • 6 slides

Determining objective biophysical provinces from multiple satellite sensor observations

NMS Final stress= 11.6 MRPP A-stat=0.21 p<0.001. 1. 3. 5. 9. NMS Final stress= 7.11 MRPP A-stat=0.42 p<0.001. 4. 7. 6. 10. 2. 8. NMS Final stress= 14.1 MRPP A-stat=0.14 p<0.002. PAPA. Determining objective biophysical provinces from multiple satellite sensor observations.

68 views • 1 slides