'Feature space' presentation slideshows

Learning with Trees

Learning with Trees. Rob Nowak. University of Wisconsin-Madison Collaborators: Rui Castro, Clay Scott, Rebecca Willett. www.ece.wisc.edu/~nowak. Artwork: Piet Mondrian. Basic Problem: Partitioning.

958 views • 87 slides

What have we learned about learning?

What have we learned about learning?. Statistical learning Mathematically rigorous, general approach R equires probabilistic expression of likelihood, prior Decision trees (classification) Learning concepts that can be expressed as logical statements

669 views • 55 slides

Overview

A Higher-Level Visual Representation For Semantic Learning In Image Databases Ismail EL SAYAD 18/07/2011. Introduction. Related w ork s. Our approach. Experiments. Conclusion and perspectives. Overview. Introduction Related works Our approach Enhanced Bag of Visual Words ( E-BOW )

595 views • 48 slides

http://www.brain-map.org

http://www.brain-map.org. A Big Thanks. Prof. Jason Bohland Quantitative Neuroscience Laboratory Boston University. Dr. Luis Ibanez Open Source Proponent, ITK Kitware Inc. Supplemental Material. Allen Mouse Brain Atlas.

669 views • 43 slides

Meaning from Text: Teaching Computers to Read

509 views • 40 slides

Likelihood Models for Template Matching Using the PDF Projection Theorem

Likelihood Models for Template Matching Using the PDF Projection Theorem. Arasanathan Thayananthan Ramanan Navaratnam Dr. Phil Torr Prof. Roberto Cipolla. Problem. Overview. Problem Motivation PDF Projection Theorem Likelihood Modelling for Chamfer Matching Experiments Conclusion.

607 views • 39 slides

Biologically Inspired Noise-Robust Speech Recognition for Both Man and Machine

Biologically Inspired Noise-Robust Speech Recognition for Both Man and Machine. Mark D. Skowronski Ph.D. Proposal University of Florida Gainesville, FL, USA. Outline. Introduction Biologically inspired algorithms Speech: Energy Redistribution Features: Human Factor Cepstral Coefficients

546 views • 37 slides

A Comparison of Methods for Transductive Transfer Learning

A Comparison of Methods for Transductive Transfer Learning. Andrew Arnold Advised by William W. Cohen Machine Learning Department School of Computer Science Carnegie Mellon University May 30, 2007. What we are able to do:. Supervised learning

557 views • 32 slides

An Overview of Remote Sensing and Image Processing

An Overview of Remote Sensing and Image Processing. by Miles Logsdon with thanks to Robin Weeks and Frank Westerlund. What is Remote Sensing?.

470 views • 32 slides

CMSC 463 Fall 2010

406 views • 32 slides

Introduction to machine learning

Introduction to machine learning. Methods, applications, etc. Overview. What is m achine l earning? Applications of machine l earning Types of algorithms Basics of statistical pattern recognition Preprocessing Feature Extraction/Selection Learning Final remarks. Overview.

1.07k views • 29 slides

Multimedia Data Mining using P-trees*

Multimedia Data Mining using P-trees*. William Perrizo,William Jockheck, Amal Perera, Dongmei Ren, Weihua Wu, Yi Zhang Computer Science Department North Dakota State University, USA MDM/KDD2002 Workshop Edmonton, Alberta, Canada July 23, 2002. * P-tree technology is patent pending by NDSU.

453 views • 27 slides

Feature Similarity

Feature Similarity. 02/11/2002 Hyung Wook Park. The table of contents. Introduction. From Images to Similarity. From Features to Similarity. Similarity Between Two Sets of Points. Introduction. In the field of image retrieval - needed achieving a compact representation of .

504 views • 26 slides

Research Presentation:

Research Presentation:. Software Release and Support Vector Machine. Seungchan Lee Intelligent Electronic Systems Human and Systems Engineering Department of Electrical and Computer Engineering. Overview. Software Release Isip_lm_tester Isip_network_builder Debugging utility : Purify

481 views • 25 slides

Kernel Methods for De-noising with Neuroimaging Application

Kernel Methods for De-noising with Neuroimaging Application. Trine Julie Abrahamsen September 8, 2009. Overview. Introduction Kernel Principal Component Analysis The Pre-image Problem Analysis of Cimbi Data Conclusions. Introduction.

382 views • 22 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

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

295 views • 13 slides

Image Segmentation Using Physical Models

261 views • 12 slides

E6886 Multimedia Security Systems Project Proposal

E6886 Multimedia Security Systems Project Proposal. View-Based & Modular Eigenspaces for Face recognition. Team. Manmohan Voniyadka Ashish Sharma Prof. Ching-Yung Lin TA Yong Wang. Goal.

253 views • 11 slides

Handwritten Signature Verification

1.7k views • 10 slides