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A comparison of event models for Naive Bayes text classification
, 1998
"... Recent work in text classification has used two different firstorder probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multivariate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey ..."
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Cited by 1025 (26 self)
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Recent work in text classification has used two different firstorder probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multivariate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e
Maxmargin Markov networks
, 2003
"... In typical classification tasks, we seek a function which assigns a label to a single object. Kernelbased approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ..."
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Cited by 604 (15 self)
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In typical classification tasks, we seek a function which assigns a label to a single object. Kernelbased approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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with loops (undirected cycles). The algorithm is an exact inference algorithm for singly connected networks the beliefs converge to the cor rect marginals in a number of iterations equal to the diameter of the graph.1 However, as Pearl noted, the same algorithm will not give the correct beliefs for mul
On Positive Harris Recurrence of Multiclass Queueing Networks: A Unified Approach Via Fluid Limit Models
 Annals of Applied Probability
, 1995
"... It is now known that the usual traffic condition (the nominal load being less than one at each station) is not sufficient for stability for a multiclass open queueing network. Although there has been some progress in establishing the stability conditions for a multiclass network, there is no unified ..."
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Cited by 357 (27 self)
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, there is no unified approach to this problem. In this paper, we prove that a queueing network is positive Harris recurrent if the corresponding fluid limit model eventually reaches zero and stays there regardless of the initial system configuration. As an application of the result, we prove that single class networks
Large scale multiple kernel learning
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... While classical kernelbased learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We s ..."
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Cited by 340 (20 self)
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While classical kernelbased learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We
Indooroutdoor image classification
 IN IEEE INTL. WORKSHOP ON CONTENTBASED ACCESS OF IMAGE AND VIDEO DATABASES
, 1998
"... We show how highlevel scene properties can be inferred from classification of lowlevel image features, specifically for the indooroutdoor scene retrieval problem. We systematically studied the features: (1) histograms in the Ohta color space (2) multiresolution, simultaneous autoregressive model ..."
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Cited by 269 (0 self)
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We show how highlevel scene properties can be inferred from classification of lowlevel image features, specifically for the indooroutdoor scene retrieval problem. We systematically studied the features: (1) histograms in the Ohta color space (2) multiresolution, simultaneous autoregressive model
CuttingPlane Training of Structural SVMs
, 2007
"... Discriminative training approaches like structural SVMs have shown much promise for building highly complex and accurate models in areas like natural language processing, protein structure prediction, and information retrieval. However, current training algorithms are computationally expensive or i ..."
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Cited by 321 (10 self)
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cuttingplane method has time complexity linear in the number of training examples, linear in the desired precision, and linear also in all other parameters. Furthermore, we present an extensive empirical evaluation of the method applied to binary classification, multiclass classification, HMM sequence
SPRINT: A scalable parallel classifier for data mining
, 1996
"... Classification is an important data mining problem. Although classification is a wellstudied problem, most of the current classification algorithms require that all or a portion of the the entire dataset remain permanently in memory. This limits their suitability for mining over large databases. ..."
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Cited by 312 (8 self)
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. We present a new decisiontreebased classification algorithm, called SPRINT that removes all of the memory restrictions, and is fast and scalable. The algorithm has also been designed to be easily parallelized, allowing many processors to work together to build a single consistent model
Unified Classification Model for Geotagging Websites
, 2012
"... The paper presents a novel approach to finding regional scopes (geotagging) of websites. It relies on a single binary classification model per region type to perform the multiclass classification and uses a variety of features of different nature that have not been yet used together for machinelearn ..."
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The paper presents a novel approach to finding regional scopes (geotagging) of websites. It relies on a single binary classification model per region type to perform the multiclass classification and uses a variety of features of different nature that have not been yet used together
Image classification for contentbased indexing
 IEEE TRANSACTIONS ON IMAGE PROCESSING
, 2001
"... Grouping images into (semantically) meaningful categories using lowlevel visual features is a challenging and important problem in contentbased image retrieval. Using binary Bayesian classifiers, we attempt to capture highlevel concepts from lowlevel image features under the constraint that the ..."
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Cited by 227 (2 self)
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Grouping images into (semantically) meaningful categories using lowlevel visual features is a challenging and important problem in contentbased image retrieval. Using binary Bayesian classifiers, we attempt to capture highlevel concepts from lowlevel image features under the constraint
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