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Substitution Matrix Based Kernel Functions for Protein Secondary Structure Prediction
 In Proceedings of ICMLA04 (International Conference on Machine Learning and Applications
, 2004
"... Different approaches to using substitution matrices in kernel functions for protein secondary structure prediction (PSSP) with support vector machines are investigated. This work introduces a number of kernel functions that calculate inner products between amino acid sequences based on the entries o ..."
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Cited by 9 (3 self)
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Different approaches to using substitution matrices in kernel functions for protein secondary structure prediction (PSSP) with support vector machines are investigated. This work introduces a number of kernel functions that calculate inner products between amino acid sequences based on the entries of a substitution matrix (SM), i.e. a matrix that contains evolutionary information about the substitutability of the different amino acids that make up proteins. The starting point is always the same, i.e. a pseudo inner product (PI) between amino acid sequences making use of a SM. It is shown what conditions a SM should satisfy in order for the PI to make sense and subsequently it is shown how a substitution distance (SD) based on the PI can be defined. Next, different ways of using both the PI and the SD in kernel functions for support vector machine (SVM) learning are discussed. In a series of experiments the different kernel functions are compared with each other and with other kernel functions that do not make use of a SM. The results show that the information contained in a SM can have a positive influence on the PSSP results, provided that it is employed in the correct way.
Weighted Kernel Functions for SVM Learning in String Domains: A Distance Function Viewpoint
 In Proceedings of ICMLC (International Conference on Machine Learning and Cybernetics
, 2005
"... This paper extends the idea of weighted distance functions to kernels and support vector machines. Here, we focus on applications that rely on sliding a window over a sequence of string data. For this type of problems it is argued that a symbolic, contextbased representation of the data should be p ..."
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Cited by 3 (2 self)
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This paper extends the idea of weighted distance functions to kernels and support vector machines. Here, we focus on applications that rely on sliding a window over a sequence of string data. For this type of problems it is argued that a symbolic, contextbased representation of the data should be preferred over a continuous, real format as this is a much more intuitive setting for working with (weighted) distance functions. It is shown how a weighted string distance can be decomposed and subsequently used in di#erent kernel functions and how these kernel functions correspond to inner products between real vectors. As a casestudy named entity recognition is used with information gain ratio as a weighting scheme.
Competitive generative models with structure learning for NLP classification tasks
, 2006
"... In this paper we show that generative models are competitive with and sometimes superior to discriminative models, when both kinds of models are allowed to learn structures that are optimal for discrimination. In particular, we compare Bayesian Networks and Conditional loglinear models on two NLP t ..."
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Cited by 2 (0 self)
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In this paper we show that generative models are competitive with and sometimes superior to discriminative models, when both kinds of models are allowed to learn structures that are optimal for discrimination. In particular, we compare Bayesian Networks and Conditional loglinear models on two NLP tasks. We observe that when the structure of the generative model encodes very strong independence assumptions (a la Naive Bayes), a discriminative model is superior, but when the generative model is allowed to weaken these independence assumptions via learning a more complex structure, it can achieve very similar or better performance than a corresponding discriminative model. In addition, as structure learning for generative models is far more efficient, they may be preferable for some tasks.
Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic Data
, 2005
"... In classification problems, machine learning algorithms often make use of the assumption that (dis)similar inputs lead to (dis)similar outputs. In this case, two questions naturally arise: what does it mean for two inputs to be similar and how can this be used in a learning algorithm? In support ..."
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Cited by 1 (0 self)
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In classification problems, machine learning algorithms often make use of the assumption that (dis)similar inputs lead to (dis)similar outputs. In this case, two questions naturally arise: what does it mean for two inputs to be similar and how can this be used in a learning algorithm? In support vector machines, similarity between input examples is implicitly expressed by a kernel function that calculates inner products in the feature space. For numerical input examples the concept of an inner product is easy to define, for discrete structures like sequences of symbolic data however these concepts are less obvious. This article describes an approach to SVM learning for symbolic data that can serve as an alternative to the bagofwords approach under certain circumstances. This latter
ContextSensitive Kernel Functions: A Comparison Between Different Context Weights
, 2005
"... This paper considers weighted kernel functions for support vector machine learning with string data. More precisely, ..."
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This paper considers weighted kernel functions for support vector machine learning with string data. More precisely,