'Linearly separable' diaporamas de présentation

Classification

1.02k views • 88 slides

Support Vector Machines

1.22k views • 83 slides

Vector Space Text Classification

Vector Space Text Classification. Adapted from Lectures by Raymond Mooney and Barbara Rosario. Text Classification. Today: Introduction to Text Classification K Nearest Neighbors Decision boundaries Vector space classification using centroids Decision Trees (briefly) Later

1.21k views • 69 slides

Linear Classifiers/SVMs

843 views • 68 slides

Support Vector Machines

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What Did We Learn?

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Neural Networks

Neural Networks. Background. - Neural Networks can be : - Biological models - Artificial models - Desire to produce artificial systems capable of sophisticated computations similar to the human brain. Biological analogy and some main ideas.

582 views • 45 slides

Classification: Linear Models

Classification: Linear Models. Prof. Navneet Goyal CS & IS BITS, Pilani. Classification. By now, you are well aware of the classification problem Assign an input vector x to one of the K discrete disjoint classes, C k

545 views • 40 slides

Support Vector Machines

698 views • 40 slides

Support Vector Machines in Data Mining AFOSR Software & Systems Annual Meeting Syracuse, NY June 3-7, 2002

Support Vector Machines in Data Mining AFOSR Software & Systems Annual Meeting Syracuse, NY June 3-7, 2002. Olvi L. Mangasarian. Data Mining Institute University of Wisconsin - Madison. What is a Support Vector Machine?. An optimally defined surface

609 views • 34 slides

CS621: Artificial Intelligence

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CISC 4631 Data Mining

CISC 4631 Data Mining. Lecture 03: Introduction to classification Linear classifier Theses slides are based on the slides by Tan, Steinbach and Kumar (textbook authors) Eamonn Koegh (UC Riverside). Classification: Definition. Given a collection of records ( training set )

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CS621: Artificial Intelligence

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The Disputed Federalist Papers : SVM Feature Selection via Concave Minimization

The Disputed Federalist Papers : SVM Feature Selection via Concave Minimization. Glenn Fung and Olvi L. Mangasarian. CSNA 2002 June 13-16, 2002 Madison, Wisconsin. Outline of Talk. Support Vector Machines (SVM) Introduction. Standard Quadratic Programming Formulation.

444 views • 22 slides

Networks and N-dimensions

Networks and N-dimensions. When to start? As we have seen, there is a continuous pattern of interest in network-style analysis, starting at least as early as McCulloch and Pitts.

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Machine Learning

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Geometry at Work

412 views • 16 slides

Optimal Energy Aware Clustering in Sensor Networks

Optimal Energy Aware Clustering in Sensor Networks. Soheil Ghiasi*, Ankur Srivastava, Xiaojian Yang, and Majid Sarrafzadeh Computer Science Department, University of California at Los Angeles SENSORS Journal July 2002 . Presented by ??. Outline. Introduction Problem Description

323 views • 15 slides

Support-Vector Networks C Cortes and V Vapnik

Support-Vector Networks C Cortes and V Vapnik. 12.04.26.(Tue) Computational Models of Intelligence Joon Shik Kim. Introduction. The support-vector network is a new learning machine for two-group classification problems.

256 views • 7 slides

Questions and Topics Review Nov. 22, 2011

Questions and Topics Review Nov. 22, 2011. Assume you have to do feature selection for a classification task. What are the characteristics of features (attributes) you might remove from the dataset prior to learning the classification algorithm ?

160 views • 5 slides