'Support vector machines' diaporamas de présentation

UCL Tutorial on: Deep Belief Nets (An updated and extended version of my 2007 NIPS tutorial)

1.89k views • 126 slides

UCL Tutorial on: Deep Belief Nets (An updated and extended version of my 2007 NIPS tutorial)

1.71k views • 126 slides

An introduction to machine learning and probabilistic graphical models

2.37k views • 88 slides

An introduction to machine learning and probabilistic graphical models

1.56k views • 88 slides

Bioinformatics: gene expression basics

Bioinformatics: gene expression basics. Ollie Rando, LRB 903. Experimental Cycle. Biological question ( hypothesis-driven or explorative). To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination:

858 views • 61 slides

Stefan Arnborg, KTH

DD2447, DD3342, spring 2010. Statistical Methods in Applied Computer Science. http://www.nada.kth.se/~stefan. Stefan Arnborg, KTH. SYLLABUS Common statistical models and their use: Bayesian, testing, and fiducial statistical philosophy Hypothesis choice Parametric inference

642 views • 41 slides

Feature Ranking and Selection for Intrusion Detection using Support Vector Machines

Feature Ranking and Selection for Intrusion Detection using Support Vector Machines. Srinivas Mukkamala & Andrew H. Sung Computer Science Department New Mexico Tech. Intrusion Data. Raw TCP/IP dump data collected form a network by simulating a typical U.S. Air Force LAN .

570 views • 38 slides

Mining Query Logs

Mining Query Logs. Team and Topic Introduction Recapitulation / Pre-requisites to understanding the Topic TF-IDF Term weighting Similarity Calculation Document Normalization What is it? How does it work? Is it used today and in what context? Relevance with Query Classification

672 views • 32 slides

Mining Query Logs

Mining Query Logs. Team and Topic Introduction Recapitulation / Pre-requisites to understanding the Topic TF-IDF Term weighting Similarity Calculation Document Normalization What is it? How does it work? Is it used today and in what context? Relevance with Query Classification

624 views • 32 slides

Support Vector Machines and The Kernel Trick

Support Vector Machines and The Kernel Trick. William Cohen 3-26-2007. ^. ^. If mistake: v k+1 = v k + y i x i. Compute: y i = v k . x i. y i. y i. The voted perceptron. instance x i. B. A. v 2. (3a) The guess v 2 after the two positive examples: v 2 = v 1 + x 2.

823 views • 31 slides

CS 4700: Foundations of Artificial Intelligence

749 views • 30 slides

CS 345A Data Mining Lecture 1

CS 345A Data Mining Lecture 1. Introduction to Web Mining. What is Web Mining?. Discovering useful information from the World-Wide Web and its usage patterns. Web Mining v. Data Mining. Structure (or lack of it) Textual information and linkage structure Scale

565 views • 29 slides

476 views • 28 slides

Y.-J. Lee, O. L. Mangasarian & W.H. Wolberg

Survival-Time Classification of Breast Cancer Patients DIMACS Workshop on Data Mining and Scalable Algorithms August 22-24, 2001- Rutgers University Y.-J. Lee, O. L. Mangasarian & W.H. Wolberg Data Mining Institute University of Wisconsin - Madison Second Annual Review June 1, 2001

446 views • 19 slides

Y.-J. Lee, O. L. Mangasarian & W.H. Wolberg

Survival-Time Classification of Breast Cancer Patients DIMACS Workshop on Data Mining and Scalable Algorithms August 22-24, 2001- Rutgers University. Y.-J. Lee, O. L. Mangasarian & W.H. Wolberg. Data Mining Institute University of Wisconsin - Madison. Second Annual Review June 1, 2001.

414 views • 19 slides

Computation Approaches to Emotional Speech

277 views • 17 slides

Tutorial 12 Linear programming Quadratic programming

Tutorial 12 Linear programming Quadratic programming. Linear case.

627 views • 16 slides

U.S. SENATE BILL CLASSIFICATION & VOTE PREDICTION

U.S. SENATE BILL CLASSIFICATION & VOTE PREDICTION. Alessandra Paulino Rick Pocklington Serhat Selcuk Bucak. GOALS . Determine the topic of a bill based on the keywords it contains Based on the content of a bill and characteristics of a senator, predict how a senator would vote for a bill

359 views • 15 slides

Optimizing Sensor Network Boundary Estimation and Localization Algorithms for TMS320C6x

Optimizing Sensor Network Boundary Estimation and Localization Algorithms for TMS320C6x. Kamal Srinivasan Niveditha Sundaram. Outline. Motivation Background Boundary Estimation Localization Project Scope Approach Conclusion. Motivation.

330 views • 13 slides

Is This Conversation on Track?

287 views • 12 slides