<--- Back to Details
First PageDocument Content
Statistics / Combinatorics / Lemmas / Algorithmic Lovász local lemma / Randomized algorithm / Mathematics / XTR / K-means clustering
Date: 2013-02-10 01:14:54
Statistics
Combinatorics
Lemmas
Algorithmic Lovász local lemma
Randomized algorithm
Mathematics
XTR
K-means clustering

A simple D 2 -sampling based PTAS for k-means and other Clustering problems Ragesh Jaiswal1 , Amit Kumar1 , and Sandeep Sen1 Department of Computer Science and Engineering, Indian Institute of Technology Delhi. {rjaiswal

Add to Reading List

Source URL: www.cse.iitd.ernet.in

Download Document from Source Website

File Size: 423,52 KB

Share Document on Facebook

Similar Documents

K-means Clustering Mohammad Emtiyaz Khan EPFL Nov 3, 2015  c

K-means Clustering Mohammad Emtiyaz Khan EPFL Nov 3, 2015 c

DocID: 1uYYl - View Document

2012 Second Joint 3DIM/3DPVT Conference: 3D Imaging, Modeling, Processing, Visualization & Transmission  Boosting the computational performance of feature-based multiple 3D scan alignment by iat-k-means clustering Nicola

2012 Second Joint 3DIM/3DPVT Conference: 3D Imaging, Modeling, Processing, Visualization & Transmission Boosting the computational performance of feature-based multiple 3D scan alignment by iat-k-means clustering Nicola

DocID: 1ux3g - View Document

Uniform Deviation Bounds for k-Means Clustering  Olivier Bachem 1 Mario Lucic 1 S. Hamed Hassani 1 Andreas Krause 1 Abstract Uniform deviation bounds limit the difference between a model’s expected loss and its loss on

Uniform Deviation Bounds for k-Means Clustering Olivier Bachem 1 Mario Lucic 1 S. Hamed Hassani 1 Andreas Krause 1 Abstract Uniform deviation bounds limit the difference between a model’s expected loss and its loss on

DocID: 1tHo7 - View Document

Uniform Deviation Bounds for k-Means Clustering Olivier Bachem 1 Mario Lucic 1 S. Hamed Hassani 1 Andreas Krause 1 Abstract Uniform deviation bounds limit the difference between a model’s expected loss and its loss on

Uniform Deviation Bounds for k-Means Clustering Olivier Bachem 1 Mario Lucic 1 S. Hamed Hassani 1 Andreas Krause 1 Abstract Uniform deviation bounds limit the difference between a model’s expected loss and its loss on

DocID: 1tDsP - View Document

Clustering: K -means and Kernel K -means Piyush Rai Machine Learning (CS771A) Aug 31, 2016  Machine Learning (CS771A)

Clustering: K -means and Kernel K -means Piyush Rai Machine Learning (CS771A) Aug 31, 2016 Machine Learning (CS771A)

DocID: 1tlmt - View Document