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Fast Effective Rule Induction

by William W. Cohen , 1995
"... Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems. We show that while IREP is extremely efficient, it frequently gives error r ..."
Abstract - Cited by 1274 (21 self) - Add to MetaCart
on 22 of 37 benchmark problems, scales nearly linearly with the number of training examples, and can efficiently process noisy datasets containing hundreds of thousands of examples.

Using Bayesian networks to analyze expression data

by Nir Friedman, Michal Linial, Iftach Nachman - Journal of Computational Biology , 2000
"... DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a “snapshot ” of transcription levels within the cell. A major challenge in computational biology is to uncover, from such measurements, gene/protein interactions and key biologica ..."
Abstract - Cited by 1088 (17 self) - Add to MetaCart
of joint multivariate probability distributions that captures properties of conditional independence between variables. Such models are attractive for their ability to describe complex stochastic processes and because they provide a clear methodology for learning from (noisy) observations. We start

Reconstruction and Representation of 3D Objects with Radial Basis Functions

by J. C. Carr, R. K. Beatson, J. B. Cherrie, T. J. Mitchell, W. R. Fright, B. C. McCallum, T. R. Evans - Computer Graphics (SIGGRAPH ’01 Conf. Proc.), pages 67–76. ACM SIGGRAPH , 2001
"... We use polyharmonic Radial Basis Functions (RBFs) to reconstruct smooth, manifold surfaces from point-cloud data and to repair incomplete meshes. An object's surface is defined implicitly as the zero set of an RBF fitted to the given surface data. Fast methods for fitting and evaluating RBFs al ..."
Abstract - Cited by 505 (1 self) - Add to MetaCart
allow us to model large data sets, consisting of millions of surface points, by a single RBF---previously an impossible task. A greedy algorithm in the fitting process reduces the number of RBF centers required to represent a surface and results in significant compression and further computational

Unsupervised Learning by Probabilistic Latent Semantic Analysis

by Thomas Hofmann - Machine Learning , 2001
"... Abstract. This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co-occurren ..."
Abstract - Cited by 618 (4 self) - Add to MetaCart
Abstract. This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co

Stable recovery of sparse overcomplete representations in the presence of noise

by David L. Donoho, Michael Elad, Vladimir N. Temlyakov - IEEE TRANS. INFORM. THEORY , 2006
"... Overcomplete representations are attracting interest in signal processing theory, particularly due to their potential to generate sparse representations of signals. However, in general, the problem of finding sparse representations must be unstable in the presence of noise. This paper establishes t ..."
Abstract - Cited by 460 (22 self) - Add to MetaCart
Overcomplete representations are attracting interest in signal processing theory, particularly due to their potential to generate sparse representations of signals. However, in general, the problem of finding sparse representations must be unstable in the presence of noise. This paper establishes

A mammalian microRNA expression atlas based on small RNA library sequencing.

by Pablo Landgraf , Mirabela Rusu , Robert Sheridan , Alain Sewer , Nicola Iovino , Alexei Aravin , Sé Bastien Pfeffer , Amanda Rice , Alice O Kamphorst , Markus Landthaler , Carolina Lin , Nicholas D Socci , Leandro Hermida , Valerio Fulci , Sabina Chiaretti , Robin Foà , Julia Schliwka , Uta Fuchs , Astrid Novosel , Roman-Ulrich Mü , Bernhard Schermer , Ute Bissels , Jason Inman , Quang Phan , David B Weir , Ruchi Choksi , Gabriella De Vita , Daniela Frezzetti , Hans-Ingo Trompeter , Veit Hornung , Grace Teng , Gunther Hartmann , Miklos Palkovits , Roberto Di Lauro , Peter Wernet , Giuseppe Macino , Charles E Rogler , James W Nagle , Jingyue Ju , F Nina Papavasiliou , Thomas Benzing , Peter Lichter , Wayne Tam , Michael J Brownstein , Andreas Bosio , James J Russo , Chris Sander , Mihaela Zavolan , Thomas Tuschl - Cell, , 2007
"... SUMMARY MicroRNAs (miRNAs) are small noncoding regulatory RNAs that reduce stability and/or translation of fully or partially sequencecomplementary target mRNAs. In order to identify miRNAs and to assess their expression patterns, we sequenced over 250 small RNA libraries from 26 different organ sy ..."
Abstract - Cited by 418 (4 self) - Add to MetaCart
systems and cell types of human and rodents that were enriched in neuronal as well as normal and malignant hematopoietic cells and tissues. We present expression profiles derived from clone count data and provide computational tools for their analysis. Unexpectedly, a relatively small set of miRNAs, many

Decision Combination in Multiple Classifier Systems

by Tin Kam Ho, Jonathan J. Hull, Sargur N. Srihari - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 16. NO. I. JANUARY 1994 , 1994
"... A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of arbitrary feature descriptors and classification procedures. Decisions by the classifiers can be represented as rankings of ..."
Abstract - Cited by 377 (5 self) - Add to MetaCart
A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of arbitrary feature descriptors and classification procedures. Decisions by the classifiers can be represented as rankings

Robust Anisotropic Diffusion

by Michael J. Black, Guillermo Sapiro, David Marimont, David Heeger , 1998
"... Relations between anisotropic diffusion and robust statistics are described in this paper. Specifically, we show that anisotropic diffusion can be seen as a robust estimation procedure that estimates a piecewise smooth image from a noisy input image. The "edge-stopping" function in the ani ..."
Abstract - Cited by 361 (17 self) - Add to MetaCart
Relations between anisotropic diffusion and robust statistics are described in this paper. Specifically, we show that anisotropic diffusion can be seen as a robust estimation procedure that estimates a piecewise smooth image from a noisy input image. The "edge-stopping" function

Tagging English Text with a Probabilistic Model

by Bernard Merialdo , 1994
"... In this paper we present some experiments on the use of a probabilistic model to tag English text, i.e. to assign to each word the correct tag (part of speech) in the context of the sentence. The main novelty of these experiments is the use of untagged text in the training of the model. We have used ..."
Abstract - Cited by 307 (0 self) - Add to MetaCart
counts, using text without tags and training the model as a hidden Markov process, according to a Maximum Likelihood principle

Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers

by Victor S. Sheng, Foster Provost, Panagiotis G. Ipeirotis
"... This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect. We examine the improvement (or lack thereof) in data quality via repeated labeling, and focus especially on the improvement of training labels for supervised induction. With the outsourcing of smal ..."
Abstract - Cited by 252 (12 self) - Add to MetaCart
-labeling strategies of increasing complexity, and show several main results. (i) Repeated-labeling can improve label quality and model quality, but not always. (ii) When labels are noisy, repeated labeling can be preferable to single labeling even in the traditional setting where labels are not particularly cheap
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