<--- Back to Details
First PageDocument Content
Statistical theory / Fisher kernel / Information retrieval / Support vector machine / Maximum likelihood / Fisher information / Kernel methods / Closed and exact differential forms / Nonlinear dimensionality reduction / Statistics / Statistical classification / Estimation theory
Date: 2005-08-03 21:23:02
Statistical theory
Fisher kernel
Information retrieval
Support vector machine
Maximum likelihood
Fisher information
Kernel methods
Closed and exact differential forms
Nonlinear dimensionality reduction
Statistics
Statistical classification
Estimation theory

Microsoft Word - f70-zhang.doc

Add to Reading List

Source URL: www.comp.nus.edu.sg

Download Document from Source Website

File Size: 200,34 KB

Share Document on Facebook

Similar Documents

Journal of Machine Learning Research490 Submitted 4/09; Revised 12/09; Published 2/10 Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization

DocID: 1ubJI - View Document

Statistics / Networks / Learning / Systems science / Statistical models / Systems biology / Machine learning / Bayesian network / Gene regulatory network / Nir Friedman / Gaussian process / Nonlinear dimensionality reduction

PROBABILISTIC MODELING OF GENE REGULATORY NETWORKS FROM DATA Thesis submitted for the degree “Doctor of Philosophy”

DocID: 1rpbq - View Document

Multivariate statistics / Numerical analysis / Statistics / Applied mathematics / Dimension reduction / T-distributed stochastic neighbor embedding / Computational statistics / Nonlinear dimensionality reduction / Mathematical optimization / Dimensionality reduction / Deep learning / Artificial neural network

Fast Optimization for t-SNE Laurens van der Maaten Department of Computer Science and Engineering, University of California, San Diego 9500 Gilman Drive, La Jolla, CA 92093, USA Pattern Recognition & Bioinformatics Lab,

DocID: 1r6Fd - View Document

Algebra / Mathematics / Multivariate statistics / Numerical analysis / Numerical linear algebra / Iterative methods / Dimension reduction / Principal component analysis / Singular value decomposition / Stochastic optimization / Nonlinear dimensionality reduction / Sparse dictionary learning

I will discuss recent work on randomized algorithms for low-rank approximation and principal component analysis (PCA). The talk will focus on efforts that move beyond the extremely fast, but relatively crude approximatio

DocID: 1qZFc - View Document

Mathematics / Statistics / Dimension reduction / Topology / Multivariate statistics / Computational statistics / Machine learning / Dimension / Nonlinear dimensionality reduction / Isomap / Semidefinite embedding / Embedding

Unsupervised Image Embedding Using Nonparametric Statistics Guobiao Mei University of California, Riverside Abstract

DocID: 1qUPd - View Document