'Training samples' presentation slideshows

Chapter 10 Unsupervised Learning & Clustering

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher. Chapter 10 Unsupervised Learning & Clustering . Introduction

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Image Classification and Others

Image Classification and Others. Ruiliang Pu Ecosystem Science Division Department of ESPM University of California, Berkeley, USA March 02, 2004. Outline. What is image classification? General classification procedures General classifiers Accuracy assessment

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Chapter 6 Associative Models

Chapter 6 Associative Models. Introduction. Associating patterns which are similar, contrary, in close proximity (spatial), in close succession (temporal) or in other relations Associative recall/retrieve evoke associated patterns recall a pattern by part of it: pattern completion

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Artificial Neural Networks

Artificial Neural Networks. Dan Simon Cleveland State University. Neural Networks. Artificial Neural Network (ANN) : An information processing paradigm that is inspired by biological neurons

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A Generalized Model for Financial Time Series Representation and Prediction

A Generalized Model for Financial Time Series Representation and Prediction. Author: Depei Bao Presenter: Liao Shu Acknowledgement: Some figures in this presentation are obtained from the paper. Outline of the Presentation. Introduction

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Chapter 4 Supervised learning: Multilayer Networks II

Chapter 4 Supervised learning: Multilayer Networks II. Other Feedforward Networks. Madaline Multiple adalines (of a sort) as hidden nodes Weight change follows minimum disturbance principle Adaptive multi-layer networks Dynamically change the network size (# of hidden nodes)

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Nearest Neighbor Editing and Condensing Techniques

Nearest Neighbor Editing and Condensing Techniques. Organization. Nearest Neighbor Revisited Condensing Techniques Proximity Graphs and Decision Boundaries Editing Techniques . Last updated: Oct. 7, 2005. Nearest Neighbour Rule. Non-parametric pattern classification.

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On the relevance of facial expressions for biometric recognition

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Document Categorization

Document Categorization. Problem: given a collection of documents, and a taxonomy of subject areas Classification : Determine the subject area(s) most pertinent to each document Indexing : Select a set of keywords / index terms appropriate to each document. Classification Techniques.

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Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection

Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection. Takafumi Kanamori Shohei Hido NIPS 2008. Outline. Motivation Importance Estimation Direct Importance Estimation Approximation Algorithm Experiments Conclusions. Motivation.

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Integration of Neural Network and Fuzzy system for Stock Price Prediction

Integration of Neural Network and Fuzzy system for Stock Price Prediction. Student : Dah-Sheng Lee Professor: Hahn-Ming Lee Date:5 December 2003. Outline. Original Network Architecture (1992)[1] GA based Fuzzy Neural Network (2001)[2][3] Quantitative model (artificial neural network)

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Chapter 5 Part II

Chapter 5 Part II. 5.3 Spread of Data 5.4 Fisher Discriminant. Measuring the spread of data. Covariance of two random variables, x and y Expectation of their product x , y need to be standardized if they use different units of measurement. Correlation.

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Learning the Relative Importance of Features in Image Data

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Radial Basis Functions

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LANDSLIDE MAPPING OBJECT-BASED APPROACH

LANDSLIDE MAPPING OBJECT-BASED APPROACH. BRYAN ZHOU MAY 9, 2013. PALOS VERDES. eCOGNITION. TRAINING SAMPLES. LEVEL 70.

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The goal is to estimate the error probability of the designed classification system Error Counting Technique Let clas

SYSTEM EVALUATION. The goal is to estimate the error probability of the designed classification system Error Counting Technique Let classes Let data points in class for testing . the number of test points. Let P i the probability error for class ? i

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Video-based Lane Detection using Boosting Principles

References. [1] J.C. McCall, and M.M. Trivedi . Video-based lane estimation and tracking: survey , IEEE Transactions on Intelligent Transportation Systems, 7(1), 2006.

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AUDIO TONALITY MODE CLASSIFICATION WITHOUT TONIC ANNOTATIONS Zhiyao Duan 1,2 , Lie Lu 1 , and Changshui Zhang 2 1. Micr

AUDIO TONALITY MODE CLASSIFICATION WITHOUT TONIC ANNOTATIONS Zhiyao Duan 1,2 , Lie Lu 1 , and Changshui Zhang 2 1. Microsoft Research Asia (MSRA), China. 2. Department of Automation, Tsinghua University, China. Summary

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http://xkcd.com/242/

http://xkcd.com/242/. Text Classification 2. David Kauchak cs160 Fall 2009 adapted from: http://www.stanford.edu/class/cs276/handouts/ lecture10-textcat-naivebayes.ppt http://www.stanford.edu/class/cs276/handouts/lecture11-vector-classify.ppt

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