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CATH  a hierarchic classification of protein domain structures
 STRUCTURE
, 1997
"... Background: Protein evolution gives rise to families of structurally related proteins, within which sequence identities can be extremely low. As a result, structurebased classifications can be effective at identifying unanticipated relationships in known structures and in optimal cases function can ..."
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Cited by 470 (33 self)
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classification of protein domain structures (CATH). The four main levels of our classification are protein class (C), architecture (A), topology (T) and homologous superfamily (H). Class is the simplest level, and it essentially describes the secondary structure composition of each domain. In contrast
A learning algorithm for Boltzmann machines
 Cognitive Science
, 1985
"... The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a probl ..."
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Cited by 584 (13 self)
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problem in o very short time. One kind of computation for which massively porollel networks appear to be well suited is large constraint satisfaction searches, but to use the connections efficiently two conditions must be met: First, a search technique that is suitable for parallel networks must be found
Waveletbased statistical signal processing using hidden Markov models
 IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 1998
"... Waveletbased statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many realworld signals. In this paper, we develop a new framework for statistical signal processing b ..."
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Cited by 415 (50 self)
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based on waveletdomain hidden Markov models (HMM’s) that concisely models the statistical dependencies and nonGaussian statistics encountered in realworld signals. Waveletdomain HMM’s are designed with the intrinsic properties of the wavelet transform in mind and provide powerful, yet tractable
A Tutorial on Learning Bayesian Networks
 Communications of the ACM
, 1995
"... We examine a graphical representation of uncertain knowledge called a Bayesian network. The representation is easy to construct and interpret, yet has formal probabilistic semantics making it suitable for statistical manipulation. We show how we can use the representation to learn new knowledge by c ..."
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Cited by 365 (12 self)
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We examine a graphical representation of uncertain knowledge called a Bayesian network. The representation is easy to construct and interpret, yet has formal probabilistic semantics making it suitable for statistical manipulation. We show how we can use the representation to learn new knowledge
Probing the Pareto frontier for basis pursuit solutions
, 2008
"... The basis pursuit problem seeks a minimum onenorm solution of an underdetermined leastsquares problem. Basis pursuit denoise (BPDN) fits the leastsquares problem only approximately, and a single parameter determines a curve that traces the optimal tradeoff between the leastsquares fit and the ..."
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Cited by 365 (5 self)
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on this curve; the algorithm is suitable for problems that are large scale and for those that are in the complex domain. At each iteration, a spectral gradientprojection method approximately minimizes a leastsquares problem with an explicit onenorm constraint. Only matrixvector operations are required
Adaptive Duplicate Detection Using Learnable String Similarity Measures
 In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2003
, 2003
"... The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we p ..."
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Cited by 344 (14 self)
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's domain. We present two learnable text similarity measures suitable for this task: an extended variant of learnable string edit distance, and a novel vectorspace based measure that employs a Support Vector Machine (SVM) for training. Experimental results on a range of datasets show that our framework can
Building DomainSpecific Embedded Languages
 ACM COMPUTING SURVEYS
, 1996
"... this paper I will describe the results of using the functional language Haskell to build DSELs. Haskell has several features that make it particularly suitable for this, but other languages could also be used. On the other hand, there are features that don't exist in any language (to my knowled ..."
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Cited by 204 (6 self)
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this paper I will describe the results of using the functional language Haskell to build DSELs. Haskell has several features that make it particularly suitable for this, but other languages could also be used. On the other hand, there are features that don't exist in any language (to my
PolySet Theory
 http://www.rbjones.com/rbjpub/pp/doc/t020.pdf. p011.tex; 25/01/2010; 13:13; p.12 13
"... This document is concerned with the specification of an interpretation of the first order language of set theory. The purpose of this is to provide an ontological basis for foundation systems suitable for the formal derivation of mathematics. The ontology is to include the pure wellfounded sets of ..."
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Cited by 259 (2 self)
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suitably large collection of pure wellfounded sets. A membership relation and a equality congruence are then defined simultaneously over this domain, so that the domain of the new intepretation is a collection of equivalence classes of these representatives. Relative to a natural semantic for the names
Selecting the Right Interestingness Measure for Association Patterns
, 2002
"... Many techniques for association rule mining and feature selection require a suitable metric to capture the dependencies among variables in a data set. For example, metrics such as support, confidence, lift, correlation, and collective strength are often used to determine the interestinghess of assoc ..."
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Cited by 254 (10 self)
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Many techniques for association rule mining and feature selection require a suitable metric to capture the dependencies among variables in a data set. For example, metrics such as support, confidence, lift, correlation, and collective strength are often used to determine the interestinghess
Results 1  10
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