'Svm training' diaporamas de présentation

Support Vector Machines

698 views • 40 slides

Landmark-Based Speech Recognition: Status Report, 7/21/2004

Landmark-Based Speech Recognition: Status Report, 7/21/2004. Status Report: Outline. Review of the paradigm Experiments that have been used in rescoring SVM: training on Switchboard vs. NTIMIT Acoustic features: MFCCs vs. rate-scale Training the pronunciation model

468 views • 34 slides

Landmark-Based Speech Recognition: Status Report, 7/21/2004

Landmark-Based Speech Recognition: Status Report, 7/21/2004. Status Report: Outline. Review of the paradigm Experiments that have been used in rescoring SVM: training on Switchboard vs. NTIMIT Acoustic features: MFCCs vs. rate-scale Training the pronunciation model

344 views • 34 slides

Large Scale Machine Learning based on MapReduce & GPU

Large Scale Machine Learning based on MapReduce & GPU. Lanbo Zhang. Motivation. Massive data challenges More and more data to process (Google: 20,000 terabytes per day) Data arrives faster and faster Solutions Invent faster ML algorithms: online algorithms

554 views • 32 slides

Guan N. Lin (Nick) Bioinformatics Intern

Bioinformatics. Prediction of Plant Protein-Protein Interaction Using sequence Only. Guan N. Lin (Nick) Bioinformatics Intern. Outline. Project Background Goals and Obstacles Tool Development & Method Design Results Analysis PPI prediction for leading genes Acknowledgements. Outline.

440 views • 32 slides

Guan N. Lin (Nick) Bioinformatics Intern

Bioinformatics. Prediction of Plant Protein-Protein Interaction Using sequence Only. Guan N. Lin (Nick) Bioinformatics Intern. Outline. Project Background Goals and Obstacles Tool Development & Method Design Results Analysis PPI prediction for leading genes Acknowledgements. Outline.

412 views • 32 slides

Landmark-Based Speech Recognition: Status Report, 7/21/2004

Landmark-Based Speech Recognition: Status Report, 7/21/2004. Status Report: Outline. Review of the paradigm Experiments that have been used in rescoring SVM: training on Switchboard vs. NTIMIT Acoustic features: MFCCs vs. rate-scale Training the pronunciation model

326 views • 32 slides

General object detection with deformable part-based models

General object detection with deformable part-based models. Many slides based on P. Felzenszwalb. Challenge: Generic object detection. Histograms of oriented gradients (HOG). Partition image into blocks at multiple scales and compute histogram of gradient orientations in each block.

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Document Categorization

Document Categorization. Problem: given a collection of documents, and a taxonomy of subject areas Classification : Determine the subject area(s) most pertinent to each document Indexing : Select a set of keywords / index terms appropriate to each document. Classification Techniques.

453 views • 25 slides

Weekly Report Start learning GPU

Weekly Report Start learning GPU. Ph.D. Student: Leo Lee date: Sep. 18, 2009. Outline. References Courses study Development Work plan. Outline. References Courses study Development Work plan. References. K-Means on commodity GPUs with CUDA

327 views • 24 slides

Using Analytic QP and Sparseness to Speed Training of Support Vector Machines

Using Analytic QP and Sparseness to Speed Training of Support Vector Machines. John C. Platt Presented by: Travis Desell. Overview. Introduction Motivation General SVMs General SVM training Related Work Sequential Minimal Optimization (SMO) Choosing the smallest optimization problem

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MSc Artificial Intelligence(University of Groningen)

MSc Artificial Intelligence(University of Groningen) Research at the Cognitive Robotics Laboratory Part of the BORG team Competing in RoboCup@Home competitions. Marc Volger. - http://www.ai.rug.nl/crl/. RoboCup@Home.

348 views • 19 slides

Fast Support Vector Machine Training and Classification on Graphics Processors

Bryan Catanzaro Narayanan Sundaram Kurt Keutzer. Fast Support Vector Machine Training and Classification on Graphics Processors. Parallel Computing Laboratory, University of California, Berkeley. Outline. Motivation Graphics Processors Support Vector Machine Training

377 views • 18 slides

Pocket, Bag, Hand, etc. - Automatically Detecting Phone Context through Discovery

Pocket, Bag, Hand, etc. - Automatically Detecting Phone Context through Discovery. Emiliano Miluzzoy, Michela Papandreax, Nicholas D. Laney, Hong Luy, Andrew T. Campbelly. Presented by: Lulwah Alkwai. Introduction Discovery Framework Phone Sensing Context Design System Implementation

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