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Statistical Comparisons of Classifiers over Multiple Data Sets

by Janez Demsar , 2006
"... While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more algorithms on multiple data sets, which is even more essential to typical machine learning studies, has been all but igno ..."
Abstract - Cited by 744 (0 self) - Add to MetaCart
While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more algorithms on multiple data sets, which is even more essential to typical machine learning studies, has been all

BIRCH: an efficient data clustering method for very large databases

by Tian Zhang, Raghu Ramakrishnan, Miron Livny - In Proc. of the ACM SIGMOD Intl. Conference on Management of Data (SIGMOD , 1996
"... Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely st,udied problems in this area is the identification of clusters, or deusel y populated regions, in a multi-dir nensional clataset. Prior work does not adequately address the problem of ..."
Abstract - Cited by 576 (2 self) - Add to MetaCart
of large datasets and minimization of 1/0 costs. This paper presents a data clustering method named Bfll (;”H (Balanced Iterative Reducing and Clustering using Hierarchies), and demonstrates that it is especially suitable for very large databases. BIRCH incrementally and clynamicall y clusters incoming

Survey on Independent Component Analysis

by Aapo Hyvärinen - NEURAL COMPUTING SURVEYS , 1999
"... A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the ..."
Abstract - Cited by 2309 (104 self) - Add to MetaCart
A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation

An analysis of transformations

by G. E. P. Box, D. R. Cox - Journal of the Royal Statistical Society. Series B (Methodological , 1964
"... In the analysis of data it is often assumed that observations y,, y,,...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal, homoscedasti ..."
Abstract - Cited by 1067 (3 self) - Add to MetaCart
In the analysis of data it is often assumed that observations y,, y,,...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal

QSplat: A Multiresolution Point Rendering System for Large Meshes

by Szymon Rusinkiewicz, Marc Levoy , 2000
"... Advances in 3D scanning technologies have enabled the practical creation of meshes with hundreds of millions of polygons. Traditional algorithms for display, simplification, and progressive transmission of meshes are impractical for data sets of this size. We describe a system for representing and p ..."
Abstract - Cited by 502 (8 self) - Add to MetaCart
be computed quickly, making it suitable for large data sets. Our implementation, written for use in a large-scale 3D digitization project, launches quickly, maintains a user-settable interactive frame rate regardless of object complexity or camera position, yields reasonable image quality during motion

OPTICS: Ordering Points To Identify the Clustering Structure

by Mihael Ankerst, Markus M. Breunig, Hans-peter Kriegel, Jörg Sander , 1999
"... Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all of ..."
Abstract - Cited by 527 (51 self) - Add to MetaCart
.g. representative points, arbitrary shaped clusters), but also the intrinsic clustering structure. For medium sized data sets, the cluster-ordering can be represented graphically and for very large data sets, we introduce an appropriate visualization technique. Both are suitable for interactive exploration

Social change and crime rate trends: a routine activity approach

by Lawrence E Cohen, Marcus Felson - American Sociological Review , 1979
"... In this paper we present a "routine activity approach " for analyzing crime rate trends and cycles. Rather than emphasizing the characteristics of offenders, with this approach we concentrate upon the circumstances in which they carry out predatory criminal acts. Most criminal acts require ..."
Abstract - Cited by 680 (5 self) - Add to MetaCart
require convergence in space and time of likely offenders, suitable targets and the absence of capable guardians against crime. Human ecological theory facilitates an investigation into the way in which social structure produces this convergence, hence allowing illegal activities to feed upon the legal

A Survey of Image Registration Techniques

by Lisa Gottesfeld Brown - ACM Computing Surveys , 1992
"... Registration is a fundamental task in image processing used to match two or more pictures taken, for example, at different times, from different sensors or from different viewpoints. Over the years, a broad range of techniques have been developed for the various types of data and problems. These ..."
Abstract - Cited by 979 (2 self) - Add to MetaCart
Registration is a fundamental task in image processing used to match two or more pictures taken, for example, at different times, from different sensors or from different viewpoints. Over the years, a broad range of techniques have been developed for the various types of data and problems

Robust principal component analysis?

by Emmanuel J Candès , Xiaodong Li , Yi Ma , John Wright - Journal of the ACM, , 2011
"... Abstract This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the ..."
Abstract - Cited by 569 (26 self) - Add to MetaCart
Abstract This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low

A Comparison of Methods for Multiclass Support Vector Machines

by Chih-Wei Hsu, Chih-Jen Lin - IEEE TRANS. NEURAL NETWORKS , 2002
"... Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary class ..."
Abstract - Cited by 952 (22 self) - Add to MetaCart
-against-one,” and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the “one-against-one” and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.
Results 1 - 10 of 27,029