PPT-Large Scale Multi-Label Classification via

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MetaLabeler Lei Tang Arizona State University Suju Rajan and Vijay K Narayanan Yahoo Data Mining amp Research Large Scale MultiLabel Classification Huge number

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Large Scale Multi-Label Classification via: Transcript

MetaLabeler Lei Tang Arizona State University Suju Rajan and Vijay K Narayanan Yahoo Data Mining amp Research Large Scale MultiLabel Classification Huge number of instances and categories. Weiqiang. . Ren. , Chong Wang, . Yanhua. Cheng, . Kaiqi. . Huang, . Tieniu. . Tan. {. wqren,cwang,yhcheng,kqhuang,tnt. }@nlpr.ia.ac.cn. Task2 : Classification + Localization. Task 2b: . Classification + localization . By. Chi . Bemieh. . Fule. August 6, 2013. THESIS PRESENTATION . Outline. . of. . today’s. presentation. Justification of the study. Problem . statement. Hypotheses. Conceptual. . framework. Research . Jia . Deng. 1,2. ,. . Nan Ding. 2. , . Yangqing. Jia. 2. , Andrea Frome. 2. , Kevin Murphy. 2. , . Samy. Bengio. 2. , Yuan Li. 2. , . Hartmut. Neven. 2. , . Hartwig. Adam. 2. University of Michigan. 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.. By . Zhangliliang. Characteristics. No . bbox. . groundtruth. needed while training. HCP infrastructure is robust to noisy. No explicit hypothesis label (reason: use CNN). Pre-train CNN from . ImageNet. Fei-Fei. Li and Olga Russakovsky. Refernce. to paper, photos, vision-lab, . stanford. logos. Olga . Russakovsky. ,. . Jia. . Deng, . Zhiheng. Huang, . Alex . Berg, Li . Fei. -. Fei. Detecting avocados to zucchinis: what have we done, and where are we going? ICCV 2013 . [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 . Weiqiang. . Ren. , Chong Wang, . Yanhua. Cheng, . Kaiqi. . Huang, . Tieniu. . Tan. {. wqren,cwang,yhcheng,kqhuang,tnt. }@nlpr.ia.ac.cn. Task2 : Classification + Localization. Task 2b: . Classification + localization . PAC Learning SVM . Kernels+Boost. Decision Trees. 1. Midterms. 2. Will be available at the TA sessions this week. Projects feedback . has been sent. . Yunchao. Wei, Wei Xia, . Junshi. Huang, . Bingbing. Ni, Jian Dong, Yao Zhao, Senior Member, IEEE . Shuicheng. Yan, Senior Member, IEEE. 2014. . arXiv. IEEE. . Short Papers. . HCPIssue. Date: Sept. 1 2016. Xueying. Bai, . Jiankun. Xu. Multi-label Image Classification. Co-occurrence dependency. Higher-order correlation: one label can be predicted using the previous label. Semantic redundancy: labels have overlapping meanings (cat and kitten). Kernels Boost. Decision Trees. 1. Midterms. 2. Will be available at the TA sessions this week. Projects feedback . has been sent. . Recall that this is 25% of your grade!. Grades are on a curve. Denis Krompaß. 1. , Maximilian Nickel. 2. and Volker Tresp. 1,3. 1. . Department of Computer Science. Ludwig Maximilian University, . 2. MIT, Cambridge and . Istituto. . Italiano. . di. . Tecnologia. Yangqiu Song. Lane . Department of CSEE. West Virginia University. 1. Much of the work was done at UIUC. Collaborators. Dan Roth . Haixun. Wang . Shusen. Wang . Weizhu. Chen. 2. Text Categorization.

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