'Inner product' diaporamas de présentation

Modeling Preference: Integrating Content-based and Collaborative Preference on Documents

Modeling Preference: Integrating Content-based and Collaborative Preference on Documents. Sung-Young Jung Intelligent Systems Program chopin@cs.pitt.edu http://www.isp.pitt.edu/~chopin/ Presentation at AI forum April 7, 2006. Contents. Introduction Concept of Preference

1.19k views • 80 slides

Margins, support vectors, and linear programming

Margins, support vectors, and linear programming. Thanks to Terran Lane and S. Dreiseitl. Exercise. Derive the vector derivative expressions: Find an expression for the minimum squared error weight vector, w , in the loss function:. Solution to LSE regression. The LSE method.

730 views • 61 slides

Linear regression models in matrix terms

691 views • 51 slides

Fast Global-Illumination on Dynamic Height Fields

743 views • 49 slides

CS276: Information Retrieval and Web Search Christopher Manning and Prabhakar Raghavan Lecture 13: Latent Semantic Inde

782 views • 46 slides

Physical Fluctuomatics Applied Stochastic Process 12th Quantum-mechanical extensions of probabilistic information proce

Physical Fluctuomatics Applied Stochastic Process 12th Quantum-mechanical extensions of probabilistic information processing. Kazuyuki Tanaka Graduate School of Information Sciences, Tohoku University kazu@smapip.is.tohoku.ac.jp http://www.smapip.is.tohoku.ac.jp/~kazu/. Contents.

549 views • 43 slides

Matrix Operations on the GPU

751 views • 38 slides

Introduction to Support Vector Machines

720 views • 38 slides

Chapter 1 Introduction to Linear systems and Matrices

Chapter 1 Introduction to Linear systems and Matrices. 1.1 Introduction. A general linear system of m equations in n unknowns looks like. A system with at least one solution is called consistent , a system with no solution at all is called inconsistent .

573 views • 37 slides

Kernels for Text Processing

Kernels for Text Processing. ??? Pum-Mo.Ryu@kaist.ac.kr. Contents. Kernels for Text Processing Kernels for Text Categorization Vector Space Kernel Latent Semantic Kernel Fisher Kernel Kernel for Syntactic Parsing Tree Kernel Conclusion. Kernels for Text Processing (1).

690 views • 37 slides

Anti-Faces for Detection

Anti-Faces for Detection. Daniel Keren Rita Osadchy Haifa University Craig Gotsman Technion. Journal Version: http://www.cs.technion.ac.il/~gotsman/AmendedPubl/Anti-Faces/anti-faces-pami.pdf. Problem Definition.

476 views • 37 slides

Deriving Private Information from Perturbed Data Using IQR-based Approach

Deriving Private Information from Perturbed Data Using IQR-based Approach. Songtao Guo UNC Charlotte Xintao Wu UNC Charlotte Yingjiu Li Singapore Management Univ . Source: http://www.privacyinternational.org/issues/foia/foia-laws.jpg. PIPEDA 2000.

458 views • 34 slides

Radial-Basis Function Networks

Radial-Basis Function Networks. A function is radial basis (RBF) if its output depends on (is a non-increasing function of) the distance of the input from a given stored vector. RBFs represent local receptors, as illustrated below, where each green point is a stored vector used in one RBF.

2.6k views • 29 slides

Fast Similarity Search for Learned Metrics

Fast Similarity Search for Learned Metrics. Prateek Jain, Brian Kulis, and Kristen Grauman Department of Computer Sciences University of Texas at Austin. Motivation. Fast image search is a useful component for a number of vision problems. Object categorization. ?. Motivation.

486 views • 27 slides

The Finite Element Method Defined

The Finite Element Method Defined. The Finite Element Method (FEM) is a weighted residual method that uses compactly-supported basis functions. Brief Comparison with Other Methods. Finite Difference (FD) Method:

1.18k views • 27 slides

Support Vector Machines

Support Vector Machines. Perceptron Revisited: Linear Separators . Binary classification can be viewed as the task of separating classes in feature space:. w T x + b = 0. w T x + b > 0. w T x + b < 0. f ( x ) = sign( w T x + b ). Linear Separators.

887 views • 21 slides

Quantum Computing Mathematics and Postulates

565 views • 21 slides

Support Vector Machines

Support Vector Machines. Perceptron Revisited: Linear Separators . Binary classification can be viewed as the task of separating classes in feature space:. w T x + b = 0. w T x + b > 0. w T x + b < 0. f ( x ) = sign( w T x + b ). Linear Separators.

490 views • 21 slides

Financial time series forecasting using support vector machines

Financial time series forecasting using support vector machines. Author: Kyoung-jae Kim 2003 Elsevier B.V. Outline. Introduction to SVM Introduction to datasets Experimental settings Analysis of experimental results. Linear separability. Linear separability

467 views • 21 slides

Physics 451

Physics 451. Quantum mechanics I Fall 2012. Oct 12, 2012 Karine Chesnel. Announcements. Homecoming. Quantum mechanics. Announcements. Homework next week: HW # 13 due Tuesday Oct 16 Pb 3.3, 3.5, A18, A19, A23, A25 HW #14 due Thursday Oct 18 Pb 3.7, 3.9, 3.10, 3.11, A26.

384 views • 11 slides