'Dimensional feature space' presentation slideshows

EE 7700

EE 7700. Pattern Classification. Classification Example. Goal : Automatically classify incoming fish according to species, and send to respective packing plants. Features : Length, width, color, brightness, etc.

924 views • 80 slides

On-Line Kernel Learning

1.05k views • 79 slides

Learning to Rank (part 1)

Learning to Rank (part 1). NESCAI 2008 Tutorial Yisong Yue Cornell University. Booming Search Industry. Goals for this Tutorial. Basics of information retrieval What machine learning contributes New challenges to address New insights on developing ML algorithms. (Soft) Prerequisites.

1.59k views • 66 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

Lecture 3: Linear Regression

Lecture 3: Linear Regression. Machine Learning CUNY Graduate Center. Today. Calculus Lagrange Multipliers Linear Regression. Optimization with constraints. What if I want to constrain the parameters of the model. The mean is less than 10

854 views • 62 slides

Classification.NET: Efficient and Accurate Classification in C#

Classification.NET: Efficient and Accurate Classification in C#. Jamie Shotton Toshiba Corporate Research & Development Center, Japan. http://jamie.shotton.org/work/code/Classification.NET.zip. Introduction. This tutorial gives brief introduction to classification theory

815 views • 55 slides

EE 4780

EE 4780. Pattern Classification. Classification Example. Goal : Automatically classify incoming fish according to species, and send to respective packing plants. Features : Length, width, color, brightness, etc.

666 views • 50 slides

CMSC 671 Fall 2001

578 views • 48 slides

Course Outline

553 views • 43 slides

Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study

Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study. Zan Huang, Hsinchun Chen, Chia-jung Hsu, Andy Chen, Soushan Wu AI Seminar Artificial Intelligence Lab The University of Arizona 08/16/2002. Agenda. Introduction Credit Risk Analysis

696 views • 41 slides

Moving Object Detection and Tracking for Intelligent Outdoor Surveillance

Moving Object Detection and Tracking for Intelligent Outdoor Surveillance. Assoc. Prof. Dr. Kanappan Palaniappan palaniappank@missouri.edu Dr. Filiz Bunyak bunyak@missouri.edu Dr. Sumit Nath naths@missouri.edu Department of Computer Science University of Missouri-Columbia.

880 views • 41 slides

Support Vector Machine & Its Applications

647 views • 34 slides

Input Space versus Feature Space in Kernel-Based Methods

Input Space versus Feature Space in Kernel-Based Methods. Scholkopf, Mika, Burges, Knirsch, Muller, Ratsch, Smola presented by: Joe Drish Department of Computer Science and Engineering University of California, San Diego. Goals. Objectives of the paper.

548 views • 23 slides

Putting non-parametric methods in the service of public health

Putting non-parametric methods in the service of public health. Seydou Doumbia, MD, PhD, Professor of Epidemiology, Department of Public Health & Deputy Director of NIAID/NIH Research Program at Malaria Research & Training Center, Faculty of Medicine, University of Bamako, Mali. INTRODUCTION.

338 views • 21 slides

Image classification

Image classification. Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing them?. Classifiers. Learn a decision rule assigning bag-of-features representations of images to different classes. Decision boundary. Zebra. Non-zebra.

407 views • 21 slides

Lecture 14: Kernels

264 views • 19 slides

Lecture 14: Kernels

330 views • 19 slides

1. Stat 231. A.L. Yuille. Fall 2004.

1. Stat 231. A.L. Yuille. Fall 2004. Perceptron Rule and Convergence Proof Capacity of Perceptrons. Multi-layer Perceptrons. Read 5.4,5.5 9.6.8 Duda, Hart, Stork. 2. Linear Separation. N samples where the Can we find a hyperplane in feature space through the origin,

268 views • 14 slides

Spectral Sequencing Based on Graph Distance

Spectral Sequencing Based on Graph Distance. Rong Liu, Hao Zhang, Oliver van Kaick {lrong, haoz, ovankaic} @ cs.sfu.ca. School of Computing Science, Simon Fraser University, Burnaby, Canada. Introduction. Problem.

156 views • 1 slides

Data mining for credit card fraud: A comparative study

Data mining for credit card fraud: A comparative study. Presenter : Cheng- Hui Chen Author : Siddhartha Bhattacharyya, Sanjeev Jha , Kurian Tharakunnel , J. Christopher Westland DSS 2010. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

471 views • 0 slides