'Linear classifier' diaporamas de présentation

Introduction to SVMs

Introduction to SVMs. SVMs. Geometric Maximizing Margin Kernel Methods Making nonlinear decision boundaries linear Efficiently! Capacity Structural Risk Minimization. Linear Classifiers. a. x. f. y est. f ( x , w ,b ) = sign( w . x - b ). denotes +1 denotes -1.

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Machine Learning CS 165B Spring 2012

Machine Learning CS 165B Spring 2012. Course outline. Introduction (Ch. 1) Concept learning (Ch. 2) Decision trees (Ch. 3) Ensemble learning Neural Networks (Ch. 4) Linear classifiers Support Vector Machines Bayesian Learning (Ch. 6) Instance-based Learning Clustering

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MaxImum entropy

MaxImum entropy. David Kauchak CS457, Spring 2011. Some material derived from Jason Eisner. Linear classifier. A linear classifier predicts the label based on a weighted, linear combination of the features

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ANGUILLA

Serbia & Montenegro (Yugoslavia). South Georgia & South Sandwich Islands. St. Helena & Dependencies. U.K. AUSTRALIA. INDIA. ANGUILLA. FRANCE. NIGER. BRAZIL. IRELAND. A Gentle Introduction to Machine Learning and Data Mining for the Database Community Dr Eamonn Keogh

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Support Vector Machines

Support Vector Machines. History. SVM is a classifier derived from statistical learning theory by Vapnik and Chervonenkis SVMs introduced by Boser, Guyon, Vapnik in COLT-92 Now an important and active field of all Machine Learning research.

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Classifiers

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Spring 2009 New Mexico Tech

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Image Classification

Image Classification. Convolutional networks - Why. Convolutions Reduce parameters Capture shift-invariance: location of patch in image should not matter Subsampling Allows greater invariance to deformations Allows the capture of large patterns with small filters.

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Support Vector Machine

Support Vector Machine. 2013.04.15 PNU Artificial Intelligence Lab. Kim, Minho. Overview. Support Vector Machines (SVM) Linear SVMs Non-Linear SVMs Properties of SVM Applications of SVMs Gene Expression Data Classification Text Categorization. Machine Learning. Type of Learning.

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Support Vector Machines

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Classification IV

Classification IV. Lecturer: Dr. Bo Yuan E-mail: yuanb@sz.tsinghua.edu.cn. Overview. Support Vector Machines. Linear Classifier. w. w ? x + b =0. w ? x + b <0. w ? x + b >0. Distance to Hyperplane. x. x '. Selection of Classifiers. ?. Which classifier is the best?

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MURI Meeting July 2002 Gert Lanckriet ( gert@eecs.berkeley )

Convex Optimization in Machine Learning. MURI Meeting July 2002 Gert Lanckriet ( gert@eecs.berkeley.edu ) L. El Ghaoui, M. Jordan, C. Bhattacharrya, N. Cristianini, P. Bartlett U.C. Berkeley. Convex Optimization in Machine Learning. Advanced Convex Optimization in Machine Learning. SDP.

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UCF REU: Weeks 1 & 2

UCF REU: Weeks 1 & 2. Gradient Code. Gradient. D irection of the Gradient: Calculating theta. Picking a threshold for the gradient map. Subtract the gradient from the original image display it . Detailing on camera. Edge Detection. Filters. Lapacian of Gaussian ( LoG ).

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Practical Online Active Learning for Classification Claire Monteleoni (MIT / UCSD)

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Component-based, Context-aware Software Systems

Component-based, Context-aware Software Systems. Workshop on Advances in Methods of Information and Communication Technology Petrosavodsk State University Michael Przybilski. Motivation. Increase of available context information Wireless information devices

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Decision making in episodic environments

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Face-Detection using Maximal Rejection Classification and Color Techniques

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Kernels

Kernels. CMPUT 466/551 Nilanjan Ray. Agenda. Kernel functions in SVM: A quick recapitulation Kernels in regression Kernels in k -nearest neighbor classifier Kernel function: a deeper understanding A case study. Kernel Functions: SVM. The dual cost function:. The non-linear classifier

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DETERMINATION OF THE PROVENANCE OF VINICA TERRA COTTA ICONS USING SUPPORT VECTOR MACHINES

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