'Testing set' diaporamas de présentation

LYU0401 Location-Based Multimedia Mobile Service

LYU0401 Location-Based Multimedia Mobile Service. Clarence Fung Tilen Ma Supervisor: Professor Michael Lyu Marker: Professor Alan Liew. Outline. Objective Introduction to Localization Area-based Probability Center of Mass Multimedia Guidance System Conclusion. Objective.

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Project Goal

Neural Computation Final Project -Earthquake Prediction 0368-4149-01, Spring 2004-5 Alon Talmor Ido Yariv. Project Goal.

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CIS671-Knowledge Discovery and Data Mining

CIS671-Knowledge Discovery and Data Mining. AI reminders. Vasileios Megalooikonomou Dept. of Computer and Information Sciences Temple University. (based on notes by Christos Faloutsos). Agenda. Statistics AI - decision trees Problem Approach Conclusions DB. Decision Trees.

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Schema Matching and Data Extraction over HTML Tables

Schema Matching and Data Extraction over HTML Tables. Cui Tao Data Extraction Research Group Department of Computer Science Brigham Young University. supported by NSF. Introduction. Many tables on the Web Ontology-based extraction: Works well for unstructured or semi-structured data

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A Fast and Scalable Nearest Neighbor Based Classification

A Fast and Scalable Nearest Neighbor Based Classification. Taufik Abidin and William Perrizo Department of Computer Science North Dakota State University. Outline. Nearest Neighbors Classification Problems

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Temple University – CIS Dept. CIS664– Knowledge Discovery and Data Mining

Temple University – CIS Dept. CIS664– Knowledge Discovery and Data Mining. V. Megalooikonomou Introduction to Decision Trees ( based on notes by Jiawei Han and Micheline Kamber and on notes by Christos Faloutsos). More details… Decision Trees. Decision trees Problem Approach

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Spatial and Temporal Data Mining

Spatial and Temporal Data Mining. V. Megalooikonomou Introduction to Decision Trees ( based on notes by Jiawei Han and Micheline Kamber and on notes by Christos Faloutsos). More details… Decision Trees. Decision trees Problem Approach Classification through trees

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IE 585

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Protein Backbone Angle Prediction with Machine Learning Approaches by R Kang, C Leslie, & A Yang

Protein Backbone Angle Prediction with Machine Learning Approaches by R Kang, C Leslie, & A Yang in Bioinformatics, 1 July 2004, vol 20 nbr 10 pp 1612-1621 with additional background information from Brad Morantz. Presented by Brad Morantz 15 November 2004. Index. Introduction

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The Unseen Challenge Data Sets

The Unseen Challenge Data Sets. Anderson Rocha Walter Scheirer Siome Goldenstein Terrance Boult. The Data Sets. Two data sets are provided PNG: lossless compression JPEG: lossy compression Prevalence of images on the Internet Sources: Google images, Yahoo Images, and Flickr.

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Testing the Limits of a QSAR Model:

Testing the Limits of a QSAR Model: How many cases are actually needed to develop a reliable predictive model? C. Matthew Sundling, Curt M. Breneman, Mark J. Embrechts, Changjian Huang, Xiaohua Wu, N. Sukumar April 9th, 2008. Related RECCR Presentations.

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Classifying Covert Photographs

Classifying Covert Photographs. CVPR 2012 POSTER. Outline. Introduction Combine Image Features and Attributes Experiment Conclusion. Introduction. Why doing this classification ? Image/video acquisition devices New Internet technologies What is covert? Secret photography.

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Different Distance Based Classification techniques on IRIS Data set

Different Distance Based Classification techniques on IRIS Data set. Data Set Description . Training Set: 60% of Each class instances Testing Set: 40% of each class Instances . Distance Metrics. Euclidean Distance (Squared ED, Normalized Square ED)

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String Barcoding

String Barcoding. Uncovering Optimal Virus Signatures. Sam Rash, Dan Gusfield University of California, Davis. Motivation. Need for rapid virus detection Given unknown virus database known viruses Problem identify unknown virus quickly Ideal solution have sequence of

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Randomization in Privacy Preserving Data Mining

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Optimizing Models Using Continuous Ant Algorithms

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REU Report Meeting Week 4

REU Report Meeting Week 4. Patrick Hynes. Topic. One shot recognition Extending to Action recognition. Animals with Attributes. Caltech 101 Experiments. Goal, compare results to Fei-fei,Fergus , Perona All 101 Categories Training set of 3,6,15 examples per class

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Biomarker panel of 7 members A2M APO AI CRP HP S100A8/S100A9 SAA SAP

B. A. Training set n = 24. Testing set n = 20. Training. Testing. SJIA. Biomarker panel of 7 members A2M APO AI CRP HP S100A8/S100A9 SAA SAP. SJIA. Clinical diagnosis. Clinical diagnosis. SJIA F. SJIA Q. SJIA F. SJIA Q. F. Q. F. Q. 12. 12. 10. 10. n =. n =.

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Predictor discovery in training set

MASS-Conductor. Prediction analysis (NSC, LDA, ROC). Cluster analysis. Predictor discovery in training set. Predictor confirmation in testing set. Pattern analysis in all set. 2. 3. 4. 1. Training set (5 CR, 5 PR of Enbrel ). Testing set (3 Anakinra CR). All set

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