'Feature generation' diaporamas de présentation

Feature Generation I

10. 9. 8. 7. 6. Left Bar. 5. 4. 3. 2. 1. 1. 2. 3. 4. 5. 6. 7. 8. 10. 9. Right Bar. Feature Generation I. Sometimes, instead of (or in addition to) searching for features, we can make new features out of combinations of old features in some way.

954 views • 95 slides

Feature Generation I

10. 9. 8. 7. 6. Left Bar. 5. 4. 3. 2. 1. 1. 2. 3. 4. 5. 6. 7. 8. 10. 9. Right Bar. Feature Generation I. Sometimes, instead of (or in addition to) searching for features, we can make new features out of combinations of old features in some way.

895 views • 88 slides

Machine Learning

526 views • 50 slides

Classification Continued Dr Eamonn Keogh

Decision Trees. Classification Continued Dr Eamonn Keogh Computer Science & Engineering Department University of California - Riverside Riverside,CA 92521 eamonn@cs.ucr.edu. Lets review the classification techniques we have seen so far, in terms of decision surfaces. 5. 4. 3. 2. 1. 0.

338 views • 31 slides

A Unified Model of Spam Filtration

William Yerazunis 1 , Shalendra Chhabra 2 , Christian Siefkes 3 , Fidelis Assis 4 , Dimitrios Gunopulos 2 1 Mitsubishi Electric Research Labs, Cambridge, MA 2 University of California Riverside, CA 3 GKVI/ FU Berlin, Germany 4 Embractel, Rio de Janeiro, Brazil.

398 views • 30 slides

Automatic Feature Generation for Endoscopic Image Classification

Automatic Feature Generation for Endoscopic Image Classification. Ulrich Klank. Endoscopic Image Classification. Synchronization of surgeries by phases is essential for Workflow Project 1 Analyzing endoscopic images supports the classification of the current state into a phase.

389 views • 27 slides

Feature Generation and Selection in SRL

Feature Generation and Selection in SRL. Alexandrin Popescul & Lyle H. Ungar Presented By Stef Schoenmackers. Overview. Structural Generalized Linear Regression (SGLR) Overview Design Motivations Experiments Conclusions. SGLR Overview. Adds statistical methods to ILP

470 views • 25 slides

EKG-Based Key Agreement in Body Sensor Networks

EKG-Based Key Agreement in Body Sensor Networks. Krishna Venkatasubramanian, Ayan Banerjee, and Sandeep Gupta IMPACT Lab Department of Computer Science and Engineering School of Computing and Informatics Ira A. Fulton School of Engineering Arizona State University Tempe, Arizona. Outline.

284 views • 17 slides

Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds

Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds. Mukund Deshpande, Michihiro Kuramochi , George Karypis University of Minnesota, Department of Computer Science/Army HPC Research Center Teacher : Dr.Ynag Student : Gun-Ren Wang Minneapolis, MN 55455

291 views • 17 slides

Enhancing Text Clustering by Leveraging Wikipedia Semantics

Enhancing Text Clustering by Leveraging Wikipedia Semantics. Presenter : Cheng- Hui Chen Authors : Jian Hu , Lujun Fang, Yang Cao, Hua -Jun Zeng , Hua Li, Qiang Yang, Zheng Chen SIGIR, 2008. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments.

255 views • 15 slides

Steganalysis with Streamwise Feature Selection

235 views • 14 slides