'Dimensionality' diaporamas de présentation

Recent developments in nonlinear dimensionality reduction

Recent developments in nonlinear dimensionality reduction. Josh Tenenbaum MIT. Collaborators. Vin de Silva John Langford Mira Bernstein Mark Steyvers Eric Berger. Outline. The problem of nonlinear dimensionality reduction The Isomap algorithm Development #1: Curved manifolds

841 views • 68 slides

MEDICAL IMAGING INFORMATICS: Lecture # 6 Segmentation

MEDICAL IMAGING INFORMATICS: Lecture # 6 Segmentation. Norbert Schuff Professor of Radiology VA Medical Center and UCSF Norbert.schuff@ucsf.edu. Overview. Definitions Role of Segmentation Segmentation methods Intensity based Shape based Texture based Summary & Conclusion Literature.

982 views • 67 slides

Computer Vision Machine Learning Features

Computer Vision Machine Learning Features. Presented By Dr. Keith Haynes. Outline. Introduction Appearance-Based Approach Features Classifiers Face Detection Walkthrough Questions. Computer Vision.

1.01k views • 63 slides

Non-Linear Unidimensional Scale Trajectories Through Multidimensional Content Spaces

Non-Linear Unidimensional Scale Trajectories Through Multidimensional Content Spaces. A Critical Examination of the Common Psychometric Claims of Unidimensionality, Linearity, and Interval-Level Measurement. Joseph Martineau Qi Diao Yang Lu Dipendra Subedi Samuel Drake Feng-Hsien Pang

802 views • 61 slides

An Introduction to the Finite Element Analysis

An Introduction to the Finite Element Analysis. Agenda. PART I Introduction and Basic Concepts 1.0 Computational Methods 1.1 Idealization 1.2 Discretization 1.3 Solution 2.0 The Finite Elements Method 2.1 FEM Notation 2.2 Element Types 3.0 Mechanichal Approach

1k views • 47 slides

Tom M. Mitchell E. Fredkin Professor and Department Head March 2007

The Discipline and Future of Machine Learning. Tom M. Mitchell E. Fredkin Professor and Department Head March 2007. The Discipline of Machine Learning. The defining question:

553 views • 42 slides

CSC2515 Fall 2007 Introduction to Machine Learning Lecture 3: Linear Classification Methods

652 views • 40 slides

Item Response Theory and Longitudinal Modeling: The Real World is Less Complicated than We Fear

Item Response Theory and Longitudinal Modeling: The Real World is Less Complicated than We Fear. Marty McCall Northwest Evaluation Association Presented to the MSDE/MARCES Conference ASSESSING & MODELING COGNITIVE DEVELOPMENT IN SCHOOL: INTELLECTUAL GROWTH AND STANDARD SETTING

495 views • 40 slides

Vector geometry: A visual tool for statistics

Vector geometry: A visual tool for statistics. Sylvain Chartier Laboratory for Computational Neurodynamics and Cognition Centre for Neural Dynamics. Vector geometry. How using a vector (arrow) we can represent concepts of

387 views • 38 slides

Final Exam Review

Final Exam Review. CS479/679 Pattern Recognition Dr. George Bebis. Final Exam Material. Midterm Exam Material Dimensionality Reduction Feature Selection Linear Discriminant Functions Support Vector Machines Expectation-Maximization Algorithm. Dimensionality Reduction.

425 views • 28 slides

The DFT-calculation of Potassium doped Picene

The DFT-calculation of Potassium doped Picene. Kotaro Yamada Kusakabe labolatory. C ontents. Introduction . ? experimental article ? what I can do ? How to Main part. ??? As a result ? output datum of K 3 Picene. ? output datum of K 2 Picene Summary & Future work. introduction.

470 views • 26 slides

A Comprehensive Study on Third Order Statistical Features for Image Splicing Detection

A Comprehensive Study on Third Order Statistical Features for Image Splicing Detection. Xudong Zhao, Shilin Wang, Shenghong Li and Jianhua Li Shanghai Jiao Tong University, Shanghai P. R. China. Introduction. Digital Image Forensics: Active detection methods

353 views • 25 slides

Renormalization of Entanglement in quantum spin models

Renormalization of Entanglement in quantum spin models. Sharif University of Technology Department of Physics. R. Jafari , M. Kargarian , A. Langari. Outlines:. Motivations Quantum Renormalization Group RG of Entanglement conclusions. Motivations.

423 views • 22 slides

Instance Based Learning

Instance Based Learning . k -Nearest Neighbor Locally weighted regression Radial basis functions Case-based reasoning Lazy and eager learning. Instance-Based Learning. When to Consider Nearest Neighbor. Instance map to points in R n Less than 20 attributes per instance

721 views • 20 slides

Units and Dimensionality

335 views • 18 slides

Using Manifold Structure for Partially Labeled Classification

Using Manifold Structure for Partially Labeled Classification. by Belkin and Niyogi, NIPS 2002. Presented by Chunping Wang Machine Learning Group, Duke University November 16, 2007. Outline. Motivations Algorithm Description Theoretical Interpretation Experimental Results Comments.

154 views • 14 slides

Hairong Qi, Gonzalez Family Professor Electrical Engineering and Computer Science

123 views • 12 slides

Manifold Learning: ISOMAP Alan O'Connor April 29, 2008

230 views • 11 slides