PPT-Large Scale Multi-Label Classification via

Author : funname | Published Date : 2020-10-06

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

Presentation Embed Code

Download Presentation

Download Presentation The PPT/PDF document "Large Scale Multi-Label Classification v..." is the property of its rightful owner. Permission is granted to download and print the materials on this website for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

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. 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. Prediction and Classification. Last week we discussed the classification problem... Used the Naïve Bayes Method. Today..we. will dive into more details... But first how do we evaluate classifier. Abstract Binary Classification Problem. Prediction and Classification. Last week we discussed the classification problem... Used the Naïve Bayes Method. Today..we. will dive into more details... But first how do we evaluate classifier. Abstract Binary Classification Problem. [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 . 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. Daniel Wichs (Northeastern University). Joint work with . Pratyay. Mukherjee. Multi-Party Computation. Goal: . Correctness. : Everyone . computes. f(x. 1. ,…,. x. n. ). . Security. :. Nothing else revealed. 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). Walker Wieland. GEOG 342. Introduction. Isocluster. Unsupervised. Interactive Supervised . Raster Analysis. Conclusions. Outline. GIS work, watershed analysis. Characterize amounts of impervious cover (IC) at spatial extents . Jeremy . Keer. Project Goals. Develop a fuzzy logic rule set to classify the content of fantasy and science fiction books based upon genre and hardness. Allow a user to classify a book with cursory knowledge with the purpose of finding whether it is similar to other styles they have enjoyed. Deepayan Chakrabarti (. deepay@fb.com. ). Stanislav Funiak. (sfuniak@fb.com). Jonathan Chang. (jonchang@fb.com). Sofus A. Macskassy (sofmac@fb.com). 1. Profile Inference. Profile:. Hometown: Palo Alto. 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.

Download Document

Here is the link to download the presentation.
"Large Scale Multi-Label Classification via"The content belongs to its owner. You may download and print it for personal use, without modification, and keep all copyright notices. By downloading, you agree to these terms.