'Kernel function' diaporamas de présentation

Chapter 7 Polynomial Regression Models

Chapter 7 Polynomial Regression Models. Ray-Bing Chen Institute of Statistics National University of Kaohsiung. 7.1 Introdution. The linear regression model y = X ? + ? is a general model for fitting any relationship that is linear in the unknown parameter ?. Polynomial regression model:.

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????. ?? ?????????????? ??????????. ??????. ??????? chen_yu@pku.edu.cn Tel ? 82529680 ??????? Tel ? 62763742 wataloo@hotmail.com ????? http://www.icst.pku.edu.cn/course/jiqixuexi/jqxx2011.mht. Ch9 Maximum Margin Classifier. Support vector machine Overlapping class distribution

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Pattern Recognition and Machine Learning

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What have we learned about learning?

What have we learned about learning?. Statistical learning Mathematically rigorous, general approach R equires probabilistic expression of likelihood, prior Decision trees (classification) Learning concepts that can be expressed as logical statements

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Rakesh Babu 200402007 Advisors : Prof C. V. Jawahar, IIIT Hyderabad

Learning Non-Linear Kernel Combinations Subject to General Regularization: Theory and Applications. Rakesh Babu 200402007 Advisors : Prof C. V. Jawahar, IIIT Hyderabad Dr. Manik Varma, Microsoft Research. Overview. Introduction to Support Vector Machines (SVM)

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L19: Advanced CUDA Issues

L19: Advanced CUDA Issues. November 10, 2009. Administrative. CLASS CANCELLED, TUESDAY, NOVEMBER 17 Guest Lecture, November 19, Ganesh Gopalakrishnan Thursday, November 12 Turn in 1 page project description (next few slides) handin cs4961 pdesc <file, ascii or PDF ok>

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An Introduction to Support Vector Machines

An Introduction to Support Vector Machines. Martin Law. Outline. History of support vector machines (SVM) Two classes, linearly separable What is a good decision boundary? Two classes, not linearly separable How to make SVM non-linear: kernel trick Demo of SVM

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CS 9633 Machine Learning Support Vector Machines

CS 9633 Machine Learning Support Vector Machines. References: Cristianini, N. and B. Scholkopf, Support Vector Machines and Kernel Methods: A New Generation of Learning Machines, AI Magazine , Fall 2002.

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Probability density estimation

Probability density estimation. Neural networks Lecture 4. Why? If we can estimate p( x ) we can estimate the class conditional probabilities P( x , | C i ) and so work out optimal (Bayesian) decision boundary . There are 3 styles of probability density estimation:

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Fisher kernels for image representation & generative classification models

Fisher kernels for image representation & generative classification models. Jakob Verbeek December 11, 2009. Plan for this course. Introduction to machine learning Clustering techniques k-means, Gaussian mixture density Gaussian mixture density continued Parameter estimation with EM

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Support Vector Machines

Support Vector Machines. Perceptron Revisited:. y( x ) = sign( w . x + b ). Linear Classifier:. w . x + b > 0. w . x + b = 0. w . x + b < 0. Which one is the best?. d. Notion of Margin. Distance from a data point to the boundary:

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A Graph-Matching Kernel for Object Categorization

A Graph-Matching Kernel for Object Categorization. Olivier Duchenne ? Armand Joulin ? Jean Ponce Willow Lab ? ICCV2011. Kernel Method. Many applications: Object recognition Text categorization time-series prediction Gene expression profile analysis .

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Recent Results in Support Vector Machines

Recent Results in Support Vector Machines. Dave Musicant. Graphic generated with Lucent Technologies Demonstration 2-D Pattern Recognition Applet at http://svm.research.bell-labs.com/SVT/SVMsvt.html. Simple Linear Perceptron. Class -1. Class 1.

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Co-Occurrence and Morphological Analysis for Colon Tissue Biopsy Classification

Co-Occurrence and Morphological Analysis for Colon Tissue Biopsy Classification. Khalid Masood, Nasir Rajpoot, Kashif Rajpoot*, Hammad Qureshi Signal and Image Processing Group, University of Warwick (UK) * Wolfson Medical Vision Lab, Oxford University (UK). Problem Definition.

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Bayesian Density Regression

Bayesian Density Regression. Author: David B. Dunson and Natesh Pillai Presenter: Ya Xue April 28, 2006. Outline. Key idea Proof Application to HME. Bayesian Density Regression with Standard DP. The regression model: (i=1,...,n) Two cases:. Parametric model.

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Parallel Programming with CUDA

Parallel Programming with CUDA. Matthew Guidry Charles McClendon. Introduction to CUDA. CUDA is a platform for performing massively parallel computations on graphics accelerators CUDA was developed by NVIDIA It was first available with their G8X line of graphics cards

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Automatic sleep stage using energy features of EEG signals

Automatic sleep stage using energy features of EEG signals . Chairman ? Hung-Chi Yang Presenter ? Yu-Kai Wang Advisor ? Yeou-Jiunn Chen Date ? 2013.10.30. Outline. Introduction Paper review Purposes Materials and Methods Future works References. Introduction. Sleep

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Identifying terms with similar meanings across corpora

Identifying terms with similar meanings across corpora . Sahami and Heilman. UN Secretary General. Kofi Annan. Google(UN Secretary General). Google(Kofi Annan). My Project. ForeignAffairs(Kofi Annan). Google(Kofi Annan). BioDatabase(Python). Google(Python). Main Program. Google Search

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