PDF-for SVMs: a case study

199 200 Fourth International Conference on Natural Computation978076953304908 2500. uchicagoedu S Sathiya Keerthi Yahoo Research Media Studios North Burbank CA 91504 USA selvarakyahooinccom ABSTRACT Large scale learning is often realistic only in a semisupervised setting where a small set of labeled exam ples is available together w The first thing to remember about writing a case study is that the case should have a problem for the readers to solve The case should have enough information in it that readers can understand what the problem is and after thinking about it and anal LESLIEANDKUANGsupportvectormachineclassiers(SVMs)andotherkernelmethodsineldsoutsidecomputationalbiology,suchastextprocessingandspeechrecognition.Forexample,thegappyn-gramkernelde-velopedbyLodhietal. Background: Neural decoding. neuron 1. neuron 2. neuron 3. neuron n. Pattern Classifier. Learning association between. neural activity an image. Background. A recent paper by Graf et al. (Nature Neuroscience . Decorelation. for clustering and classification. . ECCV 12. Bharath. . Hariharan. , . Jitandra. Malik, and Deva . Ramanan. Motivation. State-of-the-art Object Detection . HOG. Linear SVM. 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. Before deep . convnets. Using deep . convnets. PASCAL VOC. Beyond sliding windows: Region proposals. Advantages:. Cuts . down on number of regions detector must . evaluate. Allows detector to use more powerful features and classifiers. Week 10 . Presented by Christina Peterson. Movement Exemplar-SVMs . Tran and . Torresani. [1] based the MEX-SVM on the work of . Malisiewicz. . et. al. . [2]. Linear SVMs applied to histograms of space-time interest points (STIPs) calculated from . support vector machines. Perceptron. x. 1. x. 2. x. D. w. 1. w. 2. w. 3. x. 3. w. D. Input. Weights. .. .. .. Output:. . sgn. (. w. x. . + b). Can incorporate bias as component of the weight vector by always including a feature with value set to 1. Before deep . convnets. Using deep . convnets. PASCAL VOC. Beyond sliding windows: Region proposals. Advantages:. Cuts . down on number of regions detector must . evaluate. Allows detector to use more powerful features and classifiers. Animalia. Phylum:. Chordata. Class:. Mammalia. Order:. Carnivora. Family:. Felidae. Genus:. Felis. Species:. F. . catus. Cats . are similar in . anatomy . to the other . felines . with strong, flexible bodies, quick reflexes, sharp retractable claws, and teeth adapted to killing small prey. As . Bill Anderson, Gabrielle Jorns, Laura Bivens. FCS 408 - Human Development in Social Context. Catalog description --- Theories and research regarding human development and family dynamics . and their relationship to historical . Ifeoma. Nwogu. i. on. @. cs.rit.edu. Lecture . 13 . – . Classifiers for images. Schedule. Last class . RANSAC and robust line fitting. Today. Review mid-term. Start classifiers. Readings for today: . Background: Neural decoding. neuron 1. neuron 2. neuron 3. neuron n. Pattern Classifier. Learning association between. neural activity an image. Background. A recent paper by Graf et al. (Nature Neuroscience .