<--- Back to Details
First PageDocument Content
Statistics / Data analysis / Computing / Search algorithms / Covariance and correlation / Multivariate statistics / Machine learning / Mahalanobis distance / Statistical distance / Xeon / Nearest neighbor search / K-nearest neighbors algorithm
Date: 2014-10-15 09:48:22
Statistics
Data analysis
Computing
Search algorithms
Covariance and correlation
Multivariate statistics
Machine learning
Mahalanobis distance
Statistical distance
Xeon
Nearest neighbor search
K-nearest neighbors algorithm

MEMOCODE 2014 Design Contest: k-Nearest Neighbors with Mahalanobis Distance Metric Peter Milder Department of Electrical and Computer Engineering Stony Brook University Stony Brook, NY 11794–2350

Add to Reading List

Source URL: www.ece.stonybrook.edu

Download Document from Source Website

File Size: 166,27 KB

Share Document on Facebook

Similar Documents

Exploiting Search Space Structure in Classical Planning: Analyses and Algorithms (Dissertation Abstract) Masataro Asai Graduate School of Arts and Sciences University of Tokyo

Exploiting Search Space Structure in Classical Planning: Analyses and Algorithms (Dissertation Abstract) Masataro Asai Graduate School of Arts and Sciences University of Tokyo

DocID: 1xUEX - View Document

Playing with AVATAR Giles Reger, Martin Suda and Andrei Voronkov ? University of Manchester, Manchester, UK Abstract. Modern first-order resolution and superposition theorem provers use saturation algorithms to search fo

Playing with AVATAR Giles Reger, Martin Suda and Andrei Voronkov ? University of Manchester, Manchester, UK Abstract. Modern first-order resolution and superposition theorem provers use saturation algorithms to search fo

DocID: 1xTM6 - View Document

Chapter 3  Classical Optimization and Search Techniques In this chapter we discuss a few popular optimization techniques in use in current day natural language processing algorithms. First we present the Hidden Markov

Chapter 3 Classical Optimization and Search Techniques In this chapter we discuss a few popular optimization techniques in use in current day natural language processing algorithms. First we present the Hidden Markov

DocID: 1uSjw - View Document

Line Search for Averaged Operator Iteration Pontus Giselsson, Mattias F¨alt, and Stephen Boyd Abstract Many popular first order algorithms for convex optimization, such as forward-backward splitting, Douglas-Rachford sp

Line Search for Averaged Operator Iteration Pontus Giselsson, Mattias F¨alt, and Stephen Boyd Abstract Many popular first order algorithms for convex optimization, such as forward-backward splitting, Douglas-Rachford sp

DocID: 1uOGW - View Document

Structuring Depth-First Search Algorithms in Haskell David J. King John Launchbury  Department of Computing Science

Structuring Depth-First Search Algorithms in Haskell David J. King John Launchbury Department of Computing Science

DocID: 1uNEx - View Document