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A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach
 J MOL BIOL
, 2001
"... We have introduced a new method of protein secondary structure prediction which is based on the theory of support vector machine (SVM). SVM represents a new approach to supervised pattern classification which has been successfully applied to a wide range of pattern recognition problems, including ob ..."
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Cited by 134 (2 self)
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We have introduced a new method of protein secondary structure prediction which is based on the theory of support vector machine (SVM). SVM represents a new approach to supervised pattern classification which has been successfully applied to a wide range of pattern recognition problems, including object recognition, speaker identification, gene function prediction with microarray expression profile, etc. In these cases, the performance of SVM either matches or is significantly better than that of traditional machine learning approaches, including neural networks. The first use of the SVM approach to predict protein secondary structure is described here. Unlike the previous studies, we first constructed several binary classifiers, then assembled a tertiary classifier for three secondary structure states (helix, sheet and coil) based on these binary classifiers. The SVM method achieved a good performance of segment overlap accuracy SOV = 76.2 % through sevenfold cross validation on a database of 513 nonhomologous protein chains with multiple sequence alignments, which outperforms existing methods. Meanwhile threestate overall perresidue accuracy Q 3 achieved 73.5 %, which is at least comparable to existing single prediction methods. Furthermore a useful "reliability index" for the predictions was developed. In addition, SVM has many attractive features, including effective avoidance of overfitting, the ability to handle large feature spaces, information condensing of the given data set, etc. The SVM method is conveniently applied to many other pattern classification tasks in biology.
MachineLearning Research  Four Current Directions
"... Machine Learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (a) improving classification accuracy by learning ensembles of classifiers, (b) methods for scaling up super ..."
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Cited by 114 (1 self)
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Machine Learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (a) improving classification accuracy by learning ensembles of classifiers, (b) methods for scaling up supervised learning algorithms, (c) reinforcement learning, and (d) learning complex stochastic models.
A Comparative Evaluation of Voting and Metalearning on Partitioned Data
 In Proceedings of the Twelfth International Conference on Machine Learning
, 1995
"... Much of the research in inductive learning concentrates on problems with relatively small amounts of data. With the coming age of very large network computing, it is likely that orders of magnitude more data in databases will be available for various learning problems of real world importance. Some ..."
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Cited by 110 (14 self)
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Much of the research in inductive learning concentrates on problems with relatively small amounts of data. With the coming age of very large network computing, it is likely that orders of magnitude more data in databases will be available for various learning problems of real world importance. Some learning algorithms assume that the entire data set fits into main memory, which is not feasible for massive amounts of data. One approach to handling a large data set is to partition the data set into subsets, run the learning algorithm on each of the subsets, and combine the results. In this paper we evaluate different techniques for learning from partitioned data. Our metalearning approach is empirically compared with techniques in the literature that aim to combine multiple evidence to arrive at one prediction. 1 Introduction Much of the research in inductive learning concentrates on problems with relatively small amounts of data. With the coming age of very large network computing, i...
Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation
 In Advances in neural information processing systems 6
, 1994
"... Selecting a good model of a set of input points by cross validation is a computationally intensive process, especially if the number of possible models or the number of training points is high. Techniques such as gradient descent are helpful in searching through the space of models, but problems suc ..."
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Cited by 100 (9 self)
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Selecting a good model of a set of input points by cross validation is a computationally intensive process, especially if the number of possible models or the number of training points is high. Techniques such as gradient descent are helpful in searching through the space of models, but problems such as local minima, and more importantly, lack of a distance metric between various models reduce the applicability of these search methods. Hoeffding Races is a technique for finding a good model for the data by quickly discarding bad models, and concentrating the computational effort at differentiating between the better ones. This paper focuses on the special case of leaveoneout cross validation applied to memorybased learning algorithms, but we also argue that it is applicable to any class of model selection problems. 1 Introduction Model selection addresses "high level" decisions about how best to tune learning algorithm architectures for particular tasks. Such decisions include which...
Toward parallel and distributed learning by metalearning
 In Working
, 1993
"... Much of the research in inductive learning concentrates on problems with relatively small amounts of data. With the coming age of very large network computing, it is likely that orders of magnitude ..."
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Cited by 86 (26 self)
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Much of the research in inductive learning concentrates on problems with relatively small amounts of data. With the coming age of very large network computing, it is likely that orders of magnitude
Experiments on Multistrategy Learning by MetaLearning
 In Proc. Second Intl. Conference on Info. and Knowledge Mgmt
, 1993
"... In this paper, we propose metalearning as a general technique to combine the results of multiple learning algorithms, each applied to a set of training data. We detail several metalearning strategies for combining independently learned classifiers, each computed by different algorithms, to improve ..."
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Cited by 75 (13 self)
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In this paper, we propose metalearning as a general technique to combine the results of multiple learning algorithms, each applied to a set of training data. We detail several metalearning strategies for combining independently learned classifiers, each computed by different algorithms, to improve overall prediction accuracy. The overall resulting classifier is composed of the classifiers generated by the different learning algorithms and a metaclassifier generated by a metalearning strategy. The strategies described here are independent of the learning algorithms used. Preliminary experiments using different strategies and learning algorithms on two molecular biologysequence analysis data sets demonstrate encouraging results. Machine learning techniques are central to automated knowledge discovery systems and hence our approach can enhance the effectiveness of such systems. 1 Introduction The information age provides us with easy access to a wide range of information, however, it ...
Analysis of decision boundaries in linearly combined neural classifiers
 Pattern Recognition
, 1996
"... Abstract Combining or integrating the outputs of several pattern classifiers has led to improved performance in a multitude of applications. This paper provides an analytical framework to quantify the improvements in classification results due to combining. We show that combining networks linearly i ..."
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Cited by 75 (22 self)
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Abstract Combining or integrating the outputs of several pattern classifiers has led to improved performance in a multitude of applications. This paper provides an analytical framework to quantify the improvements in classification results due to combining. We show that combining networks linearly in output space reduces the variance of the actual decision region boundaries around the optimum boundary. This result is valid under the assumption that the a posteriori probability distributions for each class are locally monotonic around the Bayes optimum boundary. In the absence of classifier bias, the error is shown to be proportional to the boundary variance, resulting in a simple expression for error rate improvements. In the presence of bias, the error reduction, expressed in terms of a bias reduction factor, is shown to be less than or equal to the reduction obtained in the absence of bias. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions and combining in output space. Combining Decision boundary Neural networks Hybrid networks Variance reduction. Pattern classification 1.
Linear and Order Statistics Combiners for Pattern Classification
 Combining Artificial Neural Nets
, 1999
"... Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification resul ..."
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Cited by 65 (7 self)
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Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and order statistics combiners. We first show that to a first order approximation, the error rate obtained over and above the Bayes error rate, is directly proportional to the variance of the actual decision boundaries around the Bayes optimum boundary. Combining classifiers in output space reduces this variance, and hence reduces the "added" error. If N unbiased classifiers are combined by simple averaging, the added error rate can be reduced by a factor of N if the individual errors in approximating the decision boundaries are uncorrelated. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based nonlinear combiners, we derive expressions that indicate how much the median, the maximum and in general the ith order statistic can improve classifier performance. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions, and combining in output space. Experimental results on several public domain data sets are provided to illustrate the benefits of combining and to support the analytical results.
Improving Prediction of Protein Secondary Structure using Structured Neural Networks and Multiple Sequence Alignments
 J. Comput. Biol
, 1996
"... The prediction of protein secondary structure by use of carefully structured neural networks and multiple sequence alignments has been investigated. Separate networks are used for predicting the three secondary structures ffhelix, fistrand and coil. The networks are designed using a priori knowled ..."
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Cited by 61 (4 self)
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The prediction of protein secondary structure by use of carefully structured neural networks and multiple sequence alignments has been investigated. Separate networks are used for predicting the three secondary structures ffhelix, fistrand and coil. The networks are designed using a priori knowledge of amino acid properties with respect to the secondary structure and of the characteristic periodicity in ffhelices. Since these singlestructure networks all have less than 600 adjustable weights overfitting is avoided. To obtain a threestate prediction of ffhelix, fistrand or coil, ensembles of singlestructure networks are combined with another neural network. This method gives an overall prediction accuracy of 66.3% when using sevenfold crossvalidation on a database of 126 nonhomologous globular proteins. Applying the method to multiple sequence alignments of homologous proteins increases the prediction accuracy significantly to 71.3% with corresponding Matthews' correlation c...