Xem 1-20 trên 1691 kết quả Independent
  • [ Team LiB ] Recipe 1.11 Writing Database-Independent Code Problem You need to develop an application that can be used with different data providers, but does not lose functionality that is specific to the different providers.

    pdf3p luvpro 04-08-2010 53 4   Download

  • These criteria set a general framework for monitoring existing and proposed legislation. But monitoring the actual policy needs of SME is difficult even in advanced economies. There is a need to establish mechanisms for effective public-private sector dialogue and feedback from those who implement legislation. Business associations from time to time can undertake studies of members’ needs and governments anticipating a particularly acute problem or proposing new forms of legislation may commission surveys.

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  • This paper presents a language-independent probabilistic answer ranking framework for question answering. The framework estimates the probability of an individual answer candidate given the degree of answer relevance and the amount of supporting evidence provided in the set of answer candidates for the question. Our approach was evaluated by comparing the candidate answer sets generated by Chinese and Japanese answer extractors with the re-ranked answer sets produced by the answer ranking framework. ...

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  • In this paper we start to explore two-part collocation extraction association measures that do not estimate expected probabilities on the basis of the independence assumption. We propose two new measures based upon the well-known measures of mutual information and pointwise mutual information.

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  • We present a simple, language-independent method for integrating recovery of empty elements into syntactic parsing. This method outperforms the best published method we are aware of on English and a recently published method on Chinese.

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  • This paper describes a new discourse module within our multilingual NLP system. Because of its unique data-driven architecture, the discourse module is language-independent. Moreover, the use of hierarchically organized multiple knowledge sources makes the module robust and trainable using discourse-tagged corpora. Separating discourse phenomena from knowledge sources makes the discourse module easily extensible to additional phenomena.

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  • We present a language-pair independent terminology extraction module that is based on a sub-sentential alignment system that links linguistically motivated phrases in parallel texts. Statistical filters are applied on the bilingual list of candidate terms that is extracted from the alignment output. We compare the performance of both the alignment and terminology extraction module for three different language pairs (French-English, French-Italian and French-Dutch) and highlight languagepair specific problems (e.g. different compounding strategy in French and Dutch). ...

    pdf9p bunthai_1 06-05-2013 25 2   Download

  • Many current approaches to statistical language modeling rely on independence a.~sumptions 1)etween the different explanatory variables. This results in models which are computationally simple, but which only model the main effects of the explanatory variables oil the response variable. This paper presents an argmnent in favor of a statistical approach that also models the interactions between the explanatory variables. The argument rests on empirical evidence from two series of experiments concerning automatic ambiguity resolution. ...

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  • In this chapter, the basic concepts of independent component analysis (ICA) are defined. We start by discussing a couple of practical applications. These serve as motivation for the mathematical formulation of ICA, which is given in the form of a statistical estimation problem. Then we consider under what conditions this model can be estimated, and what exactly can be estimated.

    pdf19p duongph05 09-06-2010 69 16   Download

  • In the preceding chapters, we introduced several different estimation principles and algorithms for independent component analysis (ICA). In this chapter, we provide an overview of these methods. First, we show that all these estimation principles are intimately connected, and the main choices are between cumulant-based vs. negentropy/likelihood-based estimation methods, and between one-unit vs. multiunit methods. In other words, one must choose the nonlinearity and the decorrelation method.

    pdf17p duongph05 09-06-2010 97 16   Download

  • Tham khảo tài liệu 'independent component analysis p1', kỹ thuật - công nghệ, điện - điện tử phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

    pdf12p khinhkha 29-07-2010 55 14   Download

  • This chapter deals with applications of independent component analysis (ICA) and blind source separation (BSS) methods to telecommunications. In the following, we concentrate on code division multiple access (CDMA) techniques, because this specific branch of telecommunications provides several possibilities for applying ICA and BSS in a meaningful way.

    pdf24p duongph05 09-06-2010 75 13   Download

  • “Between modern web interfaces, server side technologies, and HTML5 games, JavaScript has never been a more important or versatile tool. To anyone just starting out with JavaScript or looking to deepen their knowledge of the practical core of the language, I would highly recommend Learning JavaScript.” —Evan Burchard, Independent Web Developer “Although I’ve read a couple of books about JavaScript before, as a backend developer, I was thrilled to see Tim Wright’s Learning JavaScript.

    pdf350p hoa_can 29-01-2013 37 12   Download

  • Chapter 3 REFINEMENTS OF THE LIMIT THEOREMS FOR NORMAL CONVERGENCE § 1 . Introduction In this chapter we consider a sequence X 1 , X2 , . . . of independent, identically distributed random variables belonging to the domain of attraction of the normal law. As shown in § 2 .6, the X; necessarily have a finite variance a 2 .

    pdf26p dalatngaymua 30-09-2010 57 10   Download

  • Chapter 7 RICHTER'S LOCAL THEOREMS AND BERNSTEIN'S INEQUALITY 1 . Statement of the theorems The theorems of this chapter do not have a collective character, and are related to Theorem 6 .1.1 . We shall consider a sequence of independent, identically distributed random variables XX

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  • In this chapter, we review central concepts of probability theory,statistics, and random processes. The emphasis is on multivariate statistics and random vectors. Matters that will be needed later in this book are discussed in more detail, including, for example, statistical independence and higher-order statistics.

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  • A difficult problem in independent component analysis (ICA) is encountered if the number of mixtures xi is smaller than the number of independent components si. This means that the mixing system is not invertible: We cannot obtain the independent components (ICs) by simply inverting the mixing matrix . Therefore, even if we knew the mixing matrix exactly, we could not recover the exact values of the independent components.

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  • Independent Component Analysis. Aapo Hyv¨ rinen, Juha Karhunen, Erkki Oja a Copyright  2001 John Wiley & Sons, Inc. ISBNs: 0-471-40540-X (Hardback); 0-471-22131-7 begin_of_the_skype_highlighting              0-471-22131-7      end_of_the_skype_highlighting (Electronic) 18 Methods using Time Structure The model of independent component analysis (ICA) that we have considered so far consists of mixing independent random variables, usually linearly. In many applications, however, what is mixed is not random variables but time signals, or time series.

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  • In this chapter, we present some additional extensions of the basic independent component analysis (ICA) model. First, we discuss the use of prior information on the mixing matrix, especially on its sparseness. Second, we present models that somewhat relax the assumption of the independence of the components.

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  • Gradients and Optimization Methods The main task in the independent component analysis (ICA) problem, formulated in Chapter 1, is to estimate a separating matrix that will give us the independent components. It also became clear that cannot generally be solved in closed form, that is, we cannot write it as some function of the sample or training set, whose value could be directly evaluated. Instead, the solution method is based on cost functions, also called objective functions or contrast functions. Solutions to ICA are found at the minima or maxima of these functions.

    pdf20p khinhkha 29-07-2010 51 9   Download


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