'Training sample' diaporamas de présentation

Classification of Microarray Gene Expression Data

Classification of Microarray Gene Expression Data. Geoff McLachlan Department of Mathematics & Institute for Molecular Bioscience University of Queensland. Institute for Molecular Bioscience, University of Queensland.

1.47k views • 127 slides

Classification of Microarray Gene Expression Data

Classification of Microarray Gene Expression Data. Geoff McLachlan Department of Mathematics & Institute for Molecular Bioscience University of Queensland. Institute for Molecular Bioscience, University of Queensland.

1.41k views • 127 slides

Stepwise Multiple Regression

Stepwise Multiple Regression. Different Methods for Entering Variables in Multiple Regression. Different types of multiple regression are distinguished by the method for entering the independent variables into the analysis.

3.13k views • 98 slides

899 views • 72 slides

I256: Applied Natural Language Processing

I256: Applied Natural Language Processing. Marti Hearst Nov 1, 2006 (Most slides originally by Barbara Rosario, modified here). Today. Algorithms for Classification Binary classification Perceptron Winnow Support Vector Machines (SVM) Kernel Methods Multi-Class classification

816 views • 65 slides

CS 9633 Machine Learning

CS 9633 Machine Learning. Computational Learning Theory. Adapted from notes by Tom Mitchell http://www-2.cs.cmu.edu/~tom/mlbook-chapter-slides.html. Theoretical Characterization of Learning Problems. Under what conditions is successful learning possible and impossible?

1.14k views • 60 slides

Physics Analysis with Advanced Data Mining Techniques

Physics Analysis with Advanced Data Mining Techniques. Hai-Jun Yang University of Michigan, Ann Arbor CCAST Workshop Beijing, November 6-10, 2006. Outline. Why Advanced Techniques ? Artificial Neural Networks (ANN) Boosted Decision Trees (BDT)

747 views • 54 slides

Why does averaging work?

Why does averaging work?. Yoav Freund AT&T Labs. Plan of talk. Generative vs. non-generative modeling Boosting Boosting and over-fitting Bagging and over-fitting Applications. Male. Human Voice. Female. Toy Example. Computer receives telephone call Measures Pitch of voice

534 views • 45 slides

Chapter 6: Backpropagation Nets

Chapter 6: Backpropagation Nets. Architecture : at least one layer of non-linear hidden units Learning : supervised, error driven, generalized delta rule Derivation of the weight update formula (with gradient descent approach) Practical considerations Variations of BP nets Applications.

731 views • 39 slides

Time-Varying Effects of Oil Supply Shocks on the US Economy

Time-Varying Effects of Oil Supply Shocks on the US Economy. Christiane Baumeister Ghent University Gert Peersman Ghent University. Motivation. The dynamic effects of oil supply shocks on the economy have probably changed over time

700 views • 26 slides

2806 Neural Computation Self-Organizing Maps Lecture 9

2806 Neural Computation Self-Organizing Maps Lecture 9. 2005 Ari Visa. Agenda. Some historical notes Some theory Self-Organizing Map Learning Vector Quantization C onclusions . Some Historical Notes . Local ordering (von der Malsbyrg, 1973)

576 views • 24 slides

Non-Bayes classifiers.

Non-Bayes classifiers. Linear discriminants, neural networks. Discriminant functions(1). Bayes classification rule:. Instead might try to find a function:. is called discriminant function. - decision surface. Class 1. Class 1. Class 2. Class 2. Discriminant functions (2).

372 views • 21 slides

Regression trees and regression graphs: Efficient estimators for Generalized Additive Models

Regression trees and regression graphs: Efficient estimators for Generalized Additive Models . Adam Tauman Kalai TTI-Chicago. Outline. Generalized Additive Models (GAM) Computationally efficient regression Model Thm: Regression graph algorithm efficiently learns GAMs

449 views • 21 slides

Pure & Applied Research: The Good, the Bad, and the Lucky

322 views • 19 slides

Classification (Supervised Clustering)

Classification (Supervised Clustering). Naomi Altman Nov '06. Objective. Starting from a sample from known groups: 1) Select a set of genes that identify the groups 2) Compute a function of the expression values that can be used to classify a new sample.

365 views • 17 slides

Missing at Random (MAR)

Missing at Random (MAR). ? is unknown parameter of the distribution for the missing-data mechanism The probability some data are missing does not depend on the actual values of the missing data

562 views • 16 slides

Optimization of Signal Significance by Bagging Decision Trees

Optimization of Signal Significance by Bagging Decision Trees. Ilya Narsky, Caltech presented by Harrison Prosper. Tools for Multivariate Classification in HEP. Physicists have some experience with linear discriminant analysis (Fisher) feedforward neural nets

371 views • 14 slides

Outline

Outline. Transmitters (Chapters 3 and 4, Source Coding and Modulation) (week 1 and 2) Receivers (Chapter 5) (week 3 and 4) Received Signal Synchronization (Chapter 6) (week 5) Channel Capacity (Chapter 7) (week 6) Error Correction Codes (Chapter 8) (week 7 and 8)

276 views • 13 slides

371 views • 12 slides

Learning Scenario

Learning Scenario. The learner considers a set of candidate hypotheses H and attempts to find the most probable one h ?H, given the observed data. Such maximally probable hypothesis is called maximum a posteriori hypothesis ( MAP ); Bayes theorem can be used to compute it:.

259 views • 10 slides