An Analysis of Single-Layer Networks in Unsupervised Feature Learning Adam Coates Stanford University Computer Science Dept.

First Page		Document Content
Date: 2014-06-02 17:06:00 K-means clustering Cluster analysis Pattern recognition Supervised learning Linear classifier Expectation–maximization algorithm Object recognition Mixture model Algorithm Statistics Machine learning Unsupervised learning		An Analysis of Single-Layer Networks in Unsupervised Feature Learning Adam Coates Stanford University Computer Science Dept. Add to Reading List Source URL: ai.stanford.edu Download Document from Source Website File Size: 391,20 KB Share Document on Facebook

	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 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 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 DocID: 1tDsP - View Document
	Clustering: K -means and Kernel K -means Piyush Rai Machine Learning (CS771A) Aug 31, 2016 Machine Learning (CS771A) DocID: 1tlmt - View Document