We propose a novel bilingual topical admixture (BiTAM) formalism for word alignment in statistical machine translation. Under this formalism, the parallel sentence-pairs within a document-pair are assumed to constitut...
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A major challenge of computational biology is the inference of genetic regulatory networks and the identification of their topology from DNA microarray data. Recent results show that scale-free networks play an import...
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A major challenge of computational biology is the inference of genetic regulatory networks and the identification of their topology from DNA microarray data. Recent results show that scale-free networks play an important role in this context. These networks are characterized by a very small number of highly connected and relevant nodes, and by numerous poorly connected ones. In this paper, we experimentally assess the predictive power of the scale-free paradigm in a supervised learning framework. The hypotheses we intend to test in this framework are: (i) regulatory genes are effective predictors of the expression of the genes they regulate;(ii) a subset of regulatory genes may explain most of the variability of the measures. More precisely, we use the expression levels of a subset of regulatory genes, returned by feature selection, as input of a learningmachine which has to predict the expression levels of the target genes. We will show that (i) each gene can be predicted by a small subset of regulatory genes, and (ii) on a global scale, a small subset of regulatory genes, called the hubs, can have a non-negligible predictive power on all the target genes. Also, most of the regulatory genes returned by the application of this approach to Gasch et al. (2000) data were identified by Segal et al. (2003) and form a biologically coherent set of genes.
A novel and simple combination of inductive logic programming with kernel methods is presented. The kFOIL algorithm integrates the well-known inductive logic programming system FOIL with kernel methods. The feature sp...
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We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs). On several large data sets, the resulting optimizer c...
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ISBN:
(纸本)1595933832
We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs). On several large data sets, the resulting optimizer converges to the same quality of solution over an order of magnitude faster than limited-memory BFGS, the leading method reported to date. We report results for both exact and inexact inference techniques.
A human listener has the ability to follow a speaker's voice while others are speaking simultaneously;in particular, the listener can organize the time-frequency energy of the same speaker across time into a singl...
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Most work on preference learning has focused on pairwise preferences or rankings over individual items. In this paper, we present a method for learning preferences over sets of items. Our learning method takes as inpu...
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ISBN:
(纸本)1595933832
Most work on preference learning has focused on pairwise preferences or rankings over individual items. In this paper, we present a method for learning preferences over sets of items. Our learning method takes as input a collection of positive examples-that is, one or more sets that have been identified by a user as desirable. Kernel density estimation is used to estimate the value function for individual items, and the desired set diversity is estimated from the average set diversity observed in the collection. Since this is a new learning problem, we introduce a new evaluation methodology and evaluate the learning method on two data collections: synthetic blocks-world data and a new real-world music data collection that we have gathered.
This paper proposes a new definition of reduction in rough sets, which follows naturally from the concepts of the degree of similarity and the degree of inconsistency. The new definition is compared to the classical d...
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This paper proposes a new definition of reduction in rough sets, which follows naturally from the concepts of the degree of similarity and the degree of inconsistency. The new definition is compared to the classical definitions-the definition in algebra view and definition in information view,and is shown to be more general.
Semi-supervised learning algorithms have been successfully applied in many applications with scarce labeled data, by utilizing the unlabeled data. One important category is graph based semi-supervised learning algorit...
Semi-supervised learning algorithms have been successfully applied in many applications with scarce labeled data, by utilizing the unlabeled data. One important category is graph based semi-supervised learning algorithms, for which the performance depends considerably on the quality of the graph, or its hyperparameters. In this paper, we deal with the less explored problem of learning the graphs. We propose a graph learning method for the harmonic energy minimization method; this is done by minimizing the leave-one-out prediction error on labeled data points. We use a gradient based method and designed an efficient algorithm which significantly accelerates the calculation of the gradient by applying the matrix inversion lemma and using careful pre-computation. Experimental results show that the graph learning method is effective in improving the performance of the classification algorithm.
We focus on the problem of estimating the graph structure associated with a discrete Markov random field. We describe a method based on ℓ1-regularized logistic regression, in which the neighborhood of any given node i...
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We focus on the problem of estimating the graph structure associated with a discrete Markov random field. We describe a method based on ℓ1-regularized logistic regression, in which the neighborhood of any given node is estimated by performing logistic regression subject to an ℓ-constraint. Our framework applies to the high-dimensional setting, in which both the number of nodes p and maximum neighborhood sizes d are allowed to grow as a function of the number of observations n. Our main result is to establish sufficient conditions on the triple (n, p, d) for the method to succeed in consistently estimating the neighborhood of every node in the graph simultaneously. Under certain mutual incoherence conditions analogous to those imposed in previous work on linear regression, we prove that consistent neighborhood selection can be obtained as long as the number of observations n grows more quickly than 6d6 log d + 2d5 log p, thereby establishing that logarithmic growth in the number of samples n relative to graph size p is sufficient to achieve neighborhood consistency.
Recently, many methods based on fuzzy rough sets are proposed to reduce fuzzy attributes. The common characteristic of these methods is that all of them are based on fuzzy equivalence relation. In other words, the und...
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Recently, many methods based on fuzzy rough sets are proposed to reduce fuzzy attributes. The common characteristic of these methods is that all of them are based on fuzzy equivalence relation. In other words, the underlying concept of rough sets, indispensability relation, is generalized to fuzzy equivalence relation. Here fuzzy equivalence relation is the binary relation, which is reflexive, symmetric and transitive. This paper tries to generalize the fuzzy equivalence relation to fuzzy similarity relation, which is more helpful to keeping the fuzzy information of initial data than fuzzy equivalence relation. Based on the fuzzy similarity relation, fuzzy matrix computation for information system is proposed which can be used to reduce fuzzy attributes. Firstly, fuzzy similarity relation who is isomorphic with the fuzzy similarity matrix is given as fuzzy indispensability relation. Then all the information of initial data, such as the similarity among objects and fuzzy inconsistence degree between two objects, can be represented by fuzzy similarity matrix. Secondly, by considering that the small perturbation of the fuzzy similarity matrix can be ignorable, we propose some basic concepts of knowledge reduction such as fuzzy attributes reduct, core and fuzzy significance of attributes etc in this paper. Thirdly, a heuristic algorithm based on the fuzzy significance of attributes is proposed to find close-to-minimal fuzzy attributes reduct. Finally, experimental comparisons with other methods of attributes reduction are given. The experimental results show that our method is feasible and effective
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