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CATH -- a hierarchic classification of protein domain structures

by C A Orengo, A D Michie, S Jones, D T Jones, M B Swindells, J M Thornton - STRUCTURE , 1997
"... Background: Protein evolution gives rise to families of structurally related proteins, within which sequence identities can be extremely low. As a result, structure-based classifications can be effective at identifying unanticipated relationships in known structures and in optimal cases function can ..."
Abstract - Cited by 470 (33 self) - Add to MetaCart
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

Understanding Normal and Impaired Word Reading: Computational Principles in Quasi-Regular Domains

by David C. Plaut , James L. McClelland, Mark S. Seidenberg, Karalyn Patterson - PSYCHOLOGICAL REVIEW , 1996
"... We develop a connectionist approach to processing in quasi-regular domains, as exemplified by English word reading. A consideration of the shortcomings of a previous implementation (Seidenberg & McClelland, 1989, Psych. Rev.) in reading nonwords leads to the development of orthographic and phono ..."
Abstract - Cited by 613 (94 self) - Add to MetaCart
We develop a connectionist approach to processing in quasi-regular domains, as exemplified by English word reading. A consideration of the shortcomings of a previous implementation (Seidenberg & McClelland, 1989, Psych. Rev.) in reading nonwords leads to the development of orthographic

ATTENTION, INTENTIONS, AND THE STRUCTURE OF DISCOURSE

by Barbara J. Grosz, Candace L. Sidner , 1986
"... In this paper we explore a new theory of discourse structure that stresses the role of purpose and processing in discourse. In this theory, discourse structure is composed of three separate but interre-lated components: the structure of the sequence of utterances (called the linguistic structure), a ..."
Abstract - Cited by 1259 (49 self) - Add to MetaCart
In this paper we explore a new theory of discourse structure that stresses the role of purpose and processing in discourse. In this theory, discourse structure is composed of three separate but interre-lated components: the structure of the sequence of utterances (called the linguistic structure

Large margin methods for structured and interdependent output variables

by Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun - JOURNAL OF MACHINE LEARNING RESEARCH , 2005
"... Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary ..."
Abstract - Cited by 624 (12 self) - Add to MetaCart
the complementary issue of designing classification algorithms that can deal with more complex outputs, such as trees, sequences, or sets. More generally, we consider problems involving multiple dependent output variables, structured output spaces, and classification problems with class attributes. In order

Fusion, Propagation, and Structuring in Belief Networks

by Judea Pearl - ARTIFICIAL INTELLIGENCE , 1986
"... Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to repre ..."
Abstract - Cited by 484 (8 self) - Add to MetaCart
to represent the generic knowledge of a domain expert, and it turns into a computational architecture if the links are used not merely for storing factual knowledge but also for directing and activating the data flow in the computations which manipulate this knowledge. The first part of the paper deals

Max-margin Markov networks

by Ben Taskar, Carlos Guestrin, Daphne Koller , 2003
"... In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based 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 ..."
Abstract - Cited by 604 (15 self) - Add to MetaCart
for learning M 3 networks based on a compact quadratic program formulation. We provide a new theoretical bound for generalization in structured domains. Experiments on the task of handwritten character recognition and collective hypertext classification demonstrate very significant gains over previous

The Pfam protein families database

by Alex Bateman, Lachlan Coin, Richard Durbin, Robert D. Finn, Volker Hollich, Ajay Khanna, Mhairi Marshall, Simon Moxon, Erik L. L. Sonnhammer, David J. Studholme, Corin Yeats, Sean R. Eddy - Nucleic Acids Res , 2002
"... Pfam is a large collection of protein families and domains. Over the past 2 years the number of families in Pfam has doubled and now stands at 6190 (version 10.0). Methodology improvements for searching the Pfam collection locally as well as via the web are described. Other recent innovations includ ..."
Abstract - Cited by 1070 (39 self) - Add to MetaCart
include modelling of discontinuous domains allow-ing Pfam domain de®nitions to be closer to those found in structure databases. Pfam is available on the web in the UK

Anomaly Detection: A Survey

by Varun Chandola, Arindam Banerjee, Vipin Kumar , 2007
"... Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and c ..."
Abstract - Cited by 540 (5 self) - Add to MetaCart
Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured

Hierarchical mixtures of experts and the EM algorithm

by Michael I. Jordan, Robert A. Jacobs , 1993
"... We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM’s). Learning is treated as a max-imum likelihood ..."
Abstract - Cited by 885 (21 self) - Add to MetaCart
We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM’s). Learning is treated as a max-imum likelihood

Learning and development in neural networks: The importance of starting small

by Jeffrey L. Elman - Cognition , 1993
"... It is a striking fact that in humans the greatest learnmg occurs precisely at that point in time- childhood- when the most dramatic maturational changes also occur. This report describes possible synergistic interactions between maturational change and the ability to learn a complex domain (language ..."
Abstract - Cited by 531 (17 self) - Add to MetaCart
It is a striking fact that in humans the greatest learnmg occurs precisely at that point in time- childhood- when the most dramatic maturational changes also occur. This report describes possible synergistic interactions between maturational change and the ability to learn a complex domain
Results 1 - 10 of 41,593