PDF-LearningFromWeaklySupervisedDatabyTheExpectationLossSVM(e-SVM)algorith

JunZhuDepartmentofStatisticsUniversityofCaliforniaLosAngelesjzhuclaeduJunhuaMaoDepartmentofStatisticsUniversityofCaliforniaLosAngelesmjhustcuclaeduAlanYuilleDepartmentofStatisticsUniversityofCal. Jia. . Jia. , . Shumei. Zhang, . Boya. Wu, . Qicong. Chen, . Juanzi. Li, . Chunxiao. Xing, and . Jie. Tang. Tsinghua University. How Do Your Friends on Social Media . Disclose. Your . Emotions. Ross . Girshick. , Jeff Donahue, Trevor Darrell, . Jitandra. Malik (UC Berkeley). Presenter: . Hossein. . Azizpour. Abstract. Can CNN improve . s.o.a. . object detection results?. Yes, it helps by learning rich representations which can then be combined with computer vision techniques.. Precision (Ranking). Incorporating High. -Order Information. Aim. Motivations and Challenges. High-Order Information. Action inside the bounding box ?. Context helps. HOB-SVM. HOAP-SVM. Encoding high-order information (joint feature map):. Leukaemia. Challenge. AGCT meeting, August 2011. David . Amar. , . Yaron. Orenstein & Ron . Zeira. Ron Shamir’s group. http://www.the-dream-project.org/challanges/dream6flowcap2-molecular-classification-acute-myeloid-leukaemia-challenge. Nemo. Exemplar. -. SVM. Still. . a. . rigid. . template. ,. but. . train. . a. . separate. . SVM. . for. . each. . positive. . instance. For each category it can has exemplar. . with. . d. David Kauchak. CS 451 – Fall 2013. Admin. Assignment 5. Midterm. Friday’s class will be in MBH 632. CS lunch talk Thursday. Java tips for the data. -. Xmx. -Xmx2g. http://. www.youtube.com. /. watch?v. Tomer. . Meshorer. Agenda. This presentation describes the use of speech recognition for:. . HCI . for spastic . dysarthria. patients . [M. Hasegawa-Johnson]. Identify progression of . P. arkinson disease using speech signal[A. Steve Branson . Oscar . Beijbom. . Serge . Belongie. CVPR 2013, Portland, Oregon. . UC San Diego. . UC San Diego. . Caltech. Overview. Structured prediction . Learning from larger datasets. Flexibility. From. Kernel . Embedding. Idea. HDLSS. . Asymptotics. & Kernel Methods. Recall . Flexibility. From. Kernel . Embedding. Idea. HDLSS. . Asymptotics. & Kernel Methods. Recall . Fox and Greg Hughes. Data Analytics – . 4600/6600. Group 3 Module 8, February 27, 2017. D. imension Reduction (DR) and Multi-Dimensional Scaling (MDS), Support Vector Machines (SVM). Dimension reduction... 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 . Ethan Grefe. December . 13, . 2013. Motivation. Spam email . is constantly cluttering inboxes. Commonly removed using rule based filters. Spam often has . very similar characteristics . This allows . Machines. Reading: . Ben-. Hur. & Weston, “A User’s Guide to Support Vector Machines”. . (linked from class web page). Notation. Assume a binary classification problem.. Instances are represented by vector . David Kauchak. CS 158 – Fall 2016. Admin. Assignment 5. back soon. write tests for your code!. variance scaling uses . standard deviation. for this class. Assignment 6. Midterm. Course feedback. Thanks!.