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137
From Boolean to Probabilistic Boolean Networks as Models of Genetic Regulatory Networks
 Proc. IEEE
, 2002
"... Mathematical and computational modeling of genetic regulatory networks promises to uncover the fundamental principles governing biological systems in an integrarive and holistic manner. It also paves the way toward the development of systematic approaches for effective therapeutic intervention in di ..."
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Cited by 122 (23 self)
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Mathematical and computational modeling of genetic regulatory networks promises to uncover the fundamental principles governing biological systems in an integrarive and holistic manner. It also paves the way toward the development of systematic approaches for effective therapeutic intervention in disease. The central theme in this paper is the Boolean formalism as a building block for modeling complex, largescale, and dynamical networks of genetic interactions. We discuss the goals of modeling genetic networks as well as the data requirements. The Boolean formalism is justified from several points of view. We then introduce Boolean networks and discuss their relationships to nonlinear digital filters. The role of Boolean networks in understanding cell differentiation and cellular functional states is discussed. The inference of Boolean networks from real gene expression data is considered from the viewpoints of computational learning theory and nonlinear signal processing, touching on computational complexity of learning and robustness. Then, a discussion of the need to handle uncertainty in a probabilistic framework is presented, leading to an introduction of probabilistic Boolean networks and their relationships to Markov chains. Methods for quantifying the influence of genes on other genes are presented. The general question of the potential effect of individual genes on the global dynamical network behavior is considered using stochastic perturbation analysis. This discussion then leads into the problem of target identification for therapeutic intervention via the development of several computational tools based on firstpassage times in Markov chains. Examples from biology are presented throughout the paper. 1
Practical selection of svm parameters and noise estimation for svm regression
 Neural Networks
, 2004
"... We investigate practical selection of metaparameters for SVM regression (that is, εinsensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than resampling approaches commonly used in SVM applications. ..."
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Cited by 111 (1 self)
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We investigate practical selection of metaparameters for SVM regression (that is, εinsensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than resampling approaches commonly used in SVM applications. Good generalization performance of the proposed parameter selection is demonstrated empirically using several lowdimensional and highdimensional regression problems. Further, we point out the importance of Vapnik’s εinsensitive loss for regression problems with finite samples. To this end, we compare generalization performance of SVM regression (with optimally chosen ε) with regression using ‘leastmodulus ’ loss (ε =0). These comparisons indicate superior generalization performance of SVM regression, for finite sample settings.
Adding semantics to detectors for video retrieval
 IEEE Transactions on Multimedia
, 2007
"... Abstract — In this paper, we propose an automatic video retrieval method based on highlevel concept detectors. Research in video analysis has reached the point where over 100 concept detectors can be learned in a generic fashion, albeit with mixed performance. Such a set of detectors is very small ..."
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Cited by 76 (14 self)
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Abstract — In this paper, we propose an automatic video retrieval method based on highlevel concept detectors. Research in video analysis has reached the point where over 100 concept detectors can be learned in a generic fashion, albeit with mixed performance. Such a set of detectors is very small still compared to ontologies aiming to capture the full vocabulary a user has. We aim to throw a bridge between the two fields by building a multimedia thesaurus, i.e. a set of machine learned concept detectors that is enriched with semantic descriptions and semantic structure obtained from WordNet. Given a multimodal user query, we identify three strategies to select a relevant detector from this thesaurus, namely: text matching, ontology querying, and semantic visual querying. We evaluate the methods against the automatic search task of the TRECVID 2005 video retrieval benchmark, using a news video archive of 85 hours in combination with a thesaurus of 363 machine learned concept detectors. We assess the influence of thesaurus size on video search performance, evaluate and compare the multimodal selection strategies for concept detectors, and finally discuss their combined potential using oracle fusion. The set of queries in the TRECVID 2005 corpus is too small to be definite in our conclusions, but the results suggest promising new lines of research. Index Terms — Video retrieval, concept learning, knowledge modeling, content analysis and indexing, multimedia information systems I.
Computational mechanics: Pattern and prediction, structure and simplicity
 Journal of Statistical Physics
, 1999
"... Computational mechanics, an approach to structural complexity, defines a process’s causal states and gives a procedure for finding them. We show that the causalstate representation—an Emachine—is the minimal one consistent with ..."
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Cited by 66 (10 self)
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Computational mechanics, an approach to structural complexity, defines a process’s causal states and gives a procedure for finding them. We show that the causalstate representation—an Emachine—is the minimal one consistent with
A novel transductive SVM for the semisupervised classification of remote sensing images
 IEEE Trans. Geoscience and Remote Sensing
, 2006
"... Abstract—This paper introduces a semisupervised classification method that exploits both labeled and unlabeled samples for addressing illposed problems with support vector machines (SVMs). The method is based on recent developments in statistical learning theory concerning transductive inference an ..."
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Cited by 64 (8 self)
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Abstract—This paper introduces a semisupervised classification method that exploits both labeled and unlabeled samples for addressing illposed problems with support vector machines (SVMs). The method is based on recent developments in statistical learning theory concerning transductive inference and in particular transductive SVMs (TSVMs). TSVMs exploit specific iterative algorithms which gradually search a reliable separating hyperplane (in the kernel space) with a transductive process that incorporates both labeled and unlabeled samples in the training phase. Based on an analysis of the properties of the TSVMs presented in the literature, a novel modified TSVM classifier designed for addressing illposed remotesensing problems is proposed. In particular, the proposed technique: 1) is based on a novel transductive procedure that exploits a weighting strategy for unlabeled patterns, based on a timedependent criterion; 2) is able to mitigate the effects of suboptimal model selection (which is unavoidable in the presence of smallsize training sets); and 3) can address multiclass cases. Experimental results confirm the effectiveness of the proposed method on a set of illposed remotesensing classification problems representing different operative conditions. Index Terms—Illposed problems, labeled and unlabeled patterns, machine learning, remote sensing, semisupervised classification, support vector machines (SVMs), transductive inference. I.
Interior point methods for massive support vector machines
, 2003
"... We investigate the use of interiorpoint methods for solving quadratic programming problems with a small number of linear constraints, where the quadratic term consists of a lowrank update to a positive semidefinite matrix. Several formulations of the support vector machine fit into this category ..."
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Cited by 56 (1 self)
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We investigate the use of interiorpoint methods for solving quadratic programming problems with a small number of linear constraints, where the quadratic term consists of a lowrank update to a positive semidefinite matrix. Several formulations of the support vector machine fit into this category. An interesting feature of these particular problems is the volume of data, which can lead to quadratic programs with between 10 and 100 million variables and, if written explicitly, a dense Q matrix. Our code is based on OOQP, an objectoriented interiorpoint code, with the linear algebra specialized for the support vector machine application. For the targeted massive problems, all of the data is stored out of core and we overlap computation and input/output to reduce overhead. Results are reported for several linear support vector machine formulations demonstrating that the method is reliable and scalable. Key words. support vector machine, interiorpoint method, linear algebra AMS subject classifications.
News Video Classification Using SVMbased Multimodal Classifiers and Combination Strategies
 In ACM Multimedia, JuanlesPins
, 2002
"... Video classification is the first step toward multimedia content understanding. When video is classified into conceptual categories, it is usually desirable to combine evidence from multiple modalities. However, combination strategies in previous studies were usually ad hoc. We investigate a metacl ..."
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Cited by 51 (7 self)
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Video classification is the first step toward multimedia content understanding. When video is classified into conceptual categories, it is usually desirable to combine evidence from multiple modalities. However, combination strategies in previous studies were usually ad hoc. We investigate a metaclassification combination strategy using Support Vector Machine, and compare it with probabilitybased strategies. Text features from closedcaptions and visual features from images are combined to classify broadcast news video. The experimental results show that combining multimodal classifiers can significantly improve recall and precision, and our metaclassification strategy gives better precision than the approach of taking the product of the posterior probabilities.
Data Mining
 TO APPEAR IN THE HANDBOOK OF TECHNOLOGY MANAGEMENT, H. BIDGOLI (ED.)
, 2010
"... The amount of data being generated and stored is growing exponentially, due in large part to the continuing advances in computer technology. This presents tremendous opportunities for those who can ..."
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Cited by 44 (1 self)
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The amount of data being generated and stored is growing exponentially, due in large part to the continuing advances in computer technology. This presents tremendous opportunities for those who can
Fast learning rates in statistical inference through aggregation
 SUBMITTED TO THE ANNALS OF STATISTICS
, 2008
"... We develop minimax optimal risk bounds for the general learning task consisting in predicting as well as the best function in a reference set G up to the smallest possible additive term, called the convergence rate. When the reference set is finite and when n denotes the size of the training data, w ..."
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Cited by 42 (8 self)
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We develop minimax optimal risk bounds for the general learning task consisting in predicting as well as the best function in a reference set G up to the smallest possible additive term, called the convergence rate. When the reference set is finite and when n denotes the size of the training data, we provide minimax convergence rates of the form C () log G  v with tight evaluation of the positive constant C and with n exact 0 < v ≤ 1, the latter value depending on the convexity of the loss function and on the level of noise in the output distribution. The risk upper bounds are based on a sequential randomized algorithm, which at each step concentrates on functions having both low risk and low variance with respect to the previous step prediction function. Our analysis puts forward the links between the probabilistic and worstcase viewpoints, and allows to obtain risk bounds unachievable with the standard statistical learning approach. One of the key idea of this work is to use probabilistic inequalities with respect to appropriate (Gibbs) distributions on the prediction function space instead of using them with respect to the distribution generating the data. The risk lower bounds are based on refinements of the Assouad lemma taking particularly into account the properties of the loss function. Our key example to illustrate the upper and lower bounds is to consider the Lqregression setting for which an exhaustive analysis of the convergence rates is given while q ranges in [1; +∞[.