'High dimensional space' diaporamas de présentation

Kernels and Margins

Kernels and Margins. Maria Florina Balcan 10/25/2011. O. X. O. X. X. O. X. X. O. O. X. X. X. O. O. X. X. O. Linear Separators. Hypothesis class of linear decision surfaces in R n. Instance space: X=R n. w.

508 views • 35 slides

Manifold Learning

Manifold Learning. Student: Ali Taalimi Advisor: Prof. Abidi 05/05/2012. When can we avoid the curse of dimensionality?. In what situation it might be have inferences from data in high dimensional, without face of curse of dimensionality. Smoothness

342 views • 33 slides

Instructor: Kai Zhang Fall 2019

Applications of Matrix-based Learning Algorithms. Instructor: Kai Zhang Fall 2019. Data Intensive Research. empirical. theoretical. simulation. data intensive. Symmetric Matrices. Kernel (Gram) matrix. Graph adjacency matrix. Kernel methods Support vector machines

650 views • 29 slides

Clustering and Testing in High-Dimensional Data

282 views • 27 slides

Synergy-Based, Data-Driven Generation of Object-Specific Grasps for Anthropomorphic Hands

Synergy-Based, Data-Driven Generation of Object-Specific Grasps for Anthropomorphic Hands. Julia Starke, Christian Eichmann, Simon Ottenhaus and Tamim Asfour. Shape and Grasp Manipulation.

264 views • 26 slides

DD HDS ? A Method for Visualization and Exploration of High-Dimensional Data

DD HDS ? A Method for Visualization and Exploration of High-Dimensional Data. Author: Sylvain Lespinats, Michel Verleysen, Senior Member, IEEE, Alain Giron, and Bernard Fertil Reporter: Wen-Cheng Tsai 2007/11/28. TNN , 2007. Outline.

273 views • 12 slides

DD HDS ? A Method for Visualization and Exploration of High-Dimensional Data

DD HDS ? A Method for Visualization and Exploration of High-Dimensional Data. Author: Sylvain Lespinats, Michel Verleysen, Senior Member, IEEE, Alain Giron, and Bernard Fertil Reporter: Wen-Cheng Tsai 2007/11/28. TNN , 2007. Outline.

123 views • 12 slides

Recognition by Appearance

Recognition by Appearance. Appearance-based recognition is a competing paradigm to features and alignment. No features are extracted! Images are represented by basis functions (eigenvectors) and their coefficients . Matching is performed on this compressed image representation.

124 views • 12 slides

Web Data Integration Using Approximate String Join

Web Data Integration Using Approximate String Join. Yingping Huang and Gregory Madey Computer Science and Engineering University of Notre Dame WWW2004, New York, 5/19/2004. Introduction. Web data integration is an important preprocessing step for web mining and data analysis.

214 views • 10 slides