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Supervised and unsupervised discretization of continuous features

by James Dougherty, Ron Kohavi, Mehran Sahami - in A. Prieditis & S. Russell, eds, Machine Learning: Proceedings of the Twelfth International Conference , 1995
"... Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization, identify de n-ing characteristics of the methods, and conduct an empirical evaluation of several methods. We compare binning, an unsupervised dis ..."
Abstract - Cited by 534 (11 self) - Add to MetaCart
discretization method, to entropy-based and purity-based methods, which are supervised algorithms. We found that the performance of the Naive-Bayes algorithm signi cantly improved when features were discretized using an entropy-based method. In fact, over the 16 tested datasets, the discretized version of Naive

Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging

by Eric Brill - Computational Linguistics , 1995
"... this paper, we will describe a simple rule-based approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study of this learni ..."
Abstract - Cited by 916 (7 self) - Add to MetaCart
this paper, we will describe a simple rule-based approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study

Irrelevant Features and the Subset Selection Problem

by George H. John, Ron Kohavi, Karl Pfleger - MACHINE LEARNING: PROCEEDINGS OF THE ELEVENTH INTERNATIONAL , 1994
"... We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features ..."
Abstract - Cited by 741 (26 self) - Add to MetaCart
not only on the features and the target concept, but also on the induction algorithm. We describe a method for feature subset selection using cross-validation that is applicable to any induction algorithm, and discuss experiments conducted with ID3 and C4.5 on artificial and real datasets.

SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries

by James Z. Wang, Jia Li, Gio Wiederhold - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2001
"... The need for efficient content-based image retrieval has increased tremendously in many application areas such as biomedicine, military, commerce, education, and Web image classification and searching. We present here SIMPLIcity (Semanticssensitive Integrated Matching for Picture LIbraries), an imag ..."
Abstract - Cited by 541 (35 self) - Add to MetaCart
), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. As in other regionbased retrieval systems, an image is represented by a set of regions, roughly corresponding to objects

The 2005 pascal visual object classes challenge

by Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frederic Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-taylor, Amos Storkey, Or Szedmak, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang , 2006
"... Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and peop ..."
Abstract - Cited by 633 (24 self) - Add to MetaCart
Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved. 1

Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456: 53–59

by David R. Bentley, Shankar Balasubramanian, Harold P. Swerdlow, Geoffrey P. Smith, John Milton, Clive G. Brown, Kevin P. Hall, Dirk J. Evers, Colin L. Barnes, Helen R, Jonathan M. Boutell, Jason Bryant, Richard J. Carter, R. Keira Cheetham, Anthony J. Cox, Darren J. Ellis, Michael R. Flatbush, Niall A. Gormley, Sean J, Leslie J. Irving, Mirian S. Karbelashvili, Scott M. Kirk, Heng Li, Klaus S. Maisinger, Lisa J. Murray, Bojan Obradovic, Tobias Ost, Michael L, Mark R. Pratt, Isabelle M. J. Rasolonjatovo, Mark T. Reed, Roberto Rigatti, Chiara Rodighiero, Mark T. Ross, Andrea Sabot, Subramanian V. Sankar, Svilen S. Tzonev, Eric H. Vermaas, Klaudia Walter, Xiaolin Wu, Lu Zhang, Mohammed D. Alam, Carole Anastasi, Ify C. Aniebo, David M. D. Bailey, Iain R, Kevin F. Benson, Claire Bevis, Phillip J. Black, Asha Boodhun, Joe S. Brennan, A. Bridgham, Rob C. Brown, Andrew A. Brown, Dale H. Buermann, Abass A. Bundu, James C. Burrows, Nigel P. Carter, Nestor Castillo, Maria Chiara, E. Catenazzi, R. Neil Cooley, Natasha R. Crake, Olubunmi O. Dada, Konstantinos D, Belen Dominguez-fern, David J. Earnshaw, Ugonna C. Egbujor, David W. Elmore, Sergey S. Etchin, Mark R. Ewan, Milan Fedurco, Louise J. Fraser, Karin V. Fuentes Fajardo, W. Scott Furey, David George, Kimberley J. Gietzen, Colin P, George S. Golda, Philip A. Granieri, David E. Green, David L. Gustafson, Nancy F. Hansen, Kevin Harnish, Christian D. Haudenschild, Narinder I. Heyer, Matthew M. Hims, Johnny T. Ho, Adrian M. Horgan, Katya Hoschler, Steve Hurwitz, Denis V. Ivanov, Maria Q. Johnson, Terena James, T. A. Huw Jones, Tzvetana H. Kerelska, Alan D. Kersey, Irina Khrebtukova, Alex P. Kindwall, Paula I. Kokko-gonzales, Anil Kumar, Marc A. Laurent, Cynthia T. Lawley, Sarah E. Lee, Xavier Lee, Arnold K. Liao, Jennifer A. Loch, Mitch Lok, Shujun Luo, Radhika M. Mammen, John W. Martin, Patrick G. Mccauley, Paul Mcnitt, Parul Mehta, Keith W. Moon, Joe W. Mullens, Taksina Newington, Zemin Ning , 2008
"... ..."
Abstract - Cited by 620 (1 self) - Add to MetaCart
Abstract not found

The emotional dog and its rational tail: a social intuitionist approach to moral judgment

by Jonathan Haidt - Psychological Review , 2001
"... This is the manuscript that was published, with only minor copy-editing alterations, as: Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral judgment. Psychological Review. 108, 814-834 Copyright 2001, American Psychological Association To obtain a repr ..."
Abstract - Cited by 629 (20 self) - Add to MetaCart
This is the manuscript that was published, with only minor copy-editing alterations, as: Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral judgment. Psychological Review. 108, 814-834 Copyright 2001, American Psychological Association To obtain a reprint of the final type-set article, please go through your library’s journal services, or contact the author directly Research on moral judgment has been dominated by rationalist models, in which moral judgment is thought to be caused by moral reasoning. Four reasons are given for considering the hypothesis that moral reasoning does not cause moral judgment; rather, moral reasoning is usually a post-hoc construction, generated after a judgment has been reached. The social intuitionist model is presented as an alternative to rationalist models. The model is a social model in that it de-emphasizes the private reasoning done by individuals, emphasizing instead the importance of social and cultural influences. The model is an intuitionist model in that it states that moral judgment is generally the result of quick, automatic evaluations (intuitions). The model is more consistent than rationalist models with recent findings in social, cultural, evolutionary, and biological psychology, as well as anthropology and primatology. Author notes

Comprehending Monads

by Philip Wadler - Mathematical Structures in Computer Science , 1992
"... Category theorists invented monads in the 1960's to concisely express certain aspects of universal algebra. Functional programmers invented list comprehensions in the 1970's to concisely express certain programs involving lists. This paper shows how list comprehensions may be generalised t ..."
Abstract - Cited by 522 (16 self) - Add to MetaCart
Category theorists invented monads in the 1960's to concisely express certain aspects of universal algebra. Functional programmers invented list comprehensions in the 1970's to concisely express certain programs involving lists. This paper shows how list comprehensions may be generalised to an arbitrary monad, and how the resulting programming feature can concisely express in a pure functional language some programs that manipulate state, handle exceptions, parse text, or invoke continuations. A new solution to the old problem of destructive array update is also presented. No knowledge of category theory is assumed.

Limma: linear models for microarray data

by Gordon K. Smyth, Matthew Ritchie, Natalie Thorne, James Wettenhall, Wei Shi - Bioinformatics and Computational Biology Solutions using R and Bioconductor , 2005
"... This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents ..."
Abstract - Cited by 759 (13 self) - Add to MetaCart
This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents

Analytic Number Theory

by Henryk Iwaniec, Emmanuel Kowalski - A.M.S COLLOQUIUM PUBL , 2004
"... ..."
Abstract - Cited by 584 (43 self) - Add to MetaCart
Abstract not found
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