'Linear kernel' diaporamas de présentation

Support Vector Machines II

Support Vector Machines II. Dr. Jianlin Cheng Department of Electrical Engineering and Computer Science University of Missouri, Columbia Fall, 2017 Slides Adapted from Book and CMU, Stanford Machine Learning Courses. The SMO Algorithm. Coordinate Ascend. Sequential minimal optimization.

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Support Vector Machine II

Support Vector Machine II. Jia-Bin Huang Virginia Tech. ECE-5424G / CS-5824. Spring 2019. Administrative. Please use piazza. No emails. HW 2 released. Support Vector Machine. Cost function Large margin classification Kernels Using an SVM. Support vector machine.

431 views • 42 slides

Link Prediction in Co-Authorship Network

Link Prediction in Co-Authorship Network. Le Nhat Minh ( A0074403N) Supervisor: Dongyuan Lu. Introduction. Link prediction Introduce future connections within the network scope Co-authorship network A network of collaborations among researchers, scientists, academic writers.

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Practical Modeling and Recognition using R G B - D C ameras

Practical Modeling and Recognition using R G B - D C ameras. Xiaofeng Ren, Dieter Fox Intel Labs, University of Washington. Joint work with Liefeng Bo, Kevin Lai, Peter Henry, Evan Herbst, Mike Krainin, Hao Du and others @ University of Washington. June 27, 2011.

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Flexible and Robust Co-Regularized Multi-Domain Graph Clustering

Flexible and Robust Co-Regularized Multi-Domain Graph Clustering. Wei Cheng 1 Xiang Zhang 2 Zhishan Guo 1 Yubao Wu 2 Patric F. Sullivan 1 Wei Wang 3 1 University of North Carolina at Chapel Hill, 2 Case Western Reserve University, 3 University of California, Los Angeles.

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Face Processing System

Face Processing System. Presented by: Harvest Jang Group meeting Fall 2002. Outline. Introduction System Architecture Pre-processing Face detection Face tracking Problems Future work. Introduction.

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FIL Team Chia-Yueh Carlton Chu 1 , Yizhao Ni 2 , Geoffrey Tan 1 , John Ashburner 1

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Learning The Discriminative Power-Invariance Trade-Off

Manik Varma Debajyoti Ray Presented by Evan and Suporn. Learning The Discriminative Power-Invariance Trade-Off. Outline. Motivation Background Learning weights Experiments Discussion. Motivation. Image Categorization Problem Descriptors No single descriptor for all tasks Use learning.

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Support Vector Machines Based Text-Dependent Speaker Verification Using HMM Supervectors

Support Vector Machines Based Text-Dependent Speaker Verification Using HMM Supervectors. Chengyu Dong France Telecom R&D Beijing 2008-01-21. Outline. Introduction HMM supervectors Normalized scores using SI HMM supervectors Experimental results Conclusions. Introduction.

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Denis Chigirev, Chris Moore, Greg Stephens & The Princeton EBC Team

Once Size Does Not Fit All: Regressor and Subject Specific Techniques for Predicting Experience in Natural Environments. Denis Chigirev, Chris Moore, Greg Stephens & The Princeton EBC Team. How do we learn in a very high dimensional setting (~35K voxels) ?. LINEAR. NONLINEAR.

282 views • 12 slides

Predicting NFL Game Outcomes: Back-Propagating MLP

Predicting NFL Game Outcomes: Back-Propagating MLP. By Paul McBride. Project Goal. To predict the outcome of NFL games. Remove human bias Create a completely objective and statistics based prediction method. Why a back-propagating MLP?.

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The Model

AATGATGC. AATGATGC. GCATCATT. TTACTACG. TTACTACG. CGTAGTAA. Gene 1. Gene 2. GENE. X. Known TFBS Predicted TFBS Different shapes indicate BSs for different TFs. Known TFBS Predicted TFBS. Gene 1. Gene 1. AACCCA. AACCCA. TTGGGT. TTGGGT. Gene 3. Gene 3. TGGGTT. AACCCA. ACCCAA.

89 views • 1 slides