PPT-Robust Multi-Kernel Classification of Uncertain and Imbalan

Theodore Trafalis joint work with R Pant Workshop on Clustering and Search Techniques in Large Scale Networks LATNA Nizhny Novgorod Russia November 4 2014 Research questions How can we handle data uncertainty in support vector classification problems. Classification Outline. Introduction, Overview. Classification using Graphs. Graph classification – Direct Product Kernel. Predictive Toxicology example dataset. Vertex classification – . Laplacian. PRESENTED BY . MUTHAPPA. Introduction. Support Vector Machines(SVMs) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis.. Kuan-Chuan. Peng. Tsuhan. Chen. 1. Introduction. Breakthrough progress in object classification.. 2. O. . Russakovsky. . et al. . ImageNet. . large scale visual recognition challenge. .. . arXiv:1409.0575, 2014.. Steven C.H. Hoi, . Rong. Jin, . Peilin. Zhao, . Tianbao. Yang. Machine Learning (2013). Presented by Audrey Cheong. Electrical & Computer Engineering. MATH 6397: Data Mining. Background - Online. with Multiple Labels. Lei Tang. , . Jianhui. Chen and . Jieping. Ye. Kernel-based Methods. Kernel-based methods . Support Vector Machine (SVM). Kernel Linear Discriminate Analysis (KLDA). Demonstrate success in various domains. 0.2 0.4 0.6 0.8 1.0 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 kernel(b) kernel(c) kernel(d) (a)blurredimage(b)no-blurredimage0.900.981.001.021.10 (5.35,3.37)(4.80,3.19)(4.71,3.22)(4.93,3.23)(5.03,3.22 Robust Control Toolbox. Reza . Alinezhad. . Aliakbar. . Afzalian. . Jan 2012. 2. Reza Alinezhad Aliakbar Afzalian. Outline. Modeling uncertainty . Uncertain Elements. Uncertain . Attributed . Graphs . Yu Su. University of California at Santa Barbara. with . Fangqiu. Han, Richard E. . Harang. , and . Xifeng. Yan . Introduction. A Fast Kernel for Attributed Graphs. Graph Kernel. [slides prises du cours cs294-10 UC Berkeley (2006 / 2009)]. http://www.cs.berkeley.edu/~jordan/courses/294-fall09. Basic Classification in ML. !!!!$$$!!!!. Spam . filtering. Character. recognition. Input . William Greene. Department of Economics. Stern School of Business. Descriptive . Statistics and Linear Regression. Model Building in Econometrics. Parameterizing the model. Nonparametric analysis. Semiparametric analysis. Presented by:. Nacer Khalil. Table of content. Introduction. Definition of robustness. Robust Kernel Density Estimation. Nonparametric . Contamination . Models. Scaled project Kernel Density Estimator. Heat map and Data stream. Outline . Problem Statement. Finding's . Ways for doing Heat maps. Multivariate KDE. Bandwidth (ways). Representation. Data Stream (Concept Drift) . Conclusions. Problem . Statments. Introduction, Overview. Classification using Graphs. Graph classification – Direct Product Kernel. Predictive Toxicology example dataset. Vertex classification – . Laplacian. Kernel. WEBKB example dataset. Florian Tramèr. Stanford University, Google, ETHZ. ML suffers from . adversarial. . examples.. 2. 90% Tabby Cat. 100% Guacamole. Adversarial noise. Robust classification is . hard! . 3. Clean. Adversarial (.