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312
Support vector machines for speaker and language recognition
 Computer Speech and Language
, 2006
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Everything Old Is New Again: A Fresh Look at Historical Approaches
 in Machine Learning. PhD thesis, MIT
, 2002
"... 2 Everything Old Is New Again: A Fresh Look at Historical ..."
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Cited by 106 (7 self)
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2 Everything Old Is New Again: A Fresh Look at Historical
Generalized Linear Discriminant Sequence Kernels For Speaker Recognition
, 2002
"... Support Vector Machines have recently shown dramatic performance gains in many application areas. We show that the same gains can be realized in the area of speaker recognition via sequence kernels. A sequence kernel provides a numerical comparison of speech utterances as entire sequences rather tha ..."
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Cited by 92 (23 self)
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Support Vector Machines have recently shown dramatic performance gains in many application areas. We show that the same gains can be realized in the area of speaker recognition via sequence kernels. A sequence kernel provides a numerical comparison of speech utterances as entire sequences rather than a probability at the frame level. We introduce a novel sequence kernel derived from generalized linear discriminants. The kernel has several advantages. First, the kernel uses an explicit expansion into "feature space"this property allows all of the support vectors to be collapsed into a single vector creating a small speaker model. Second, the kernel retains the computational advantage of generalized linear discriminants trained using meansquared error training. Finally, the kernel shows dramatic reductions in equal error rates over standard meansquared error training in matched and mismatched conditions on a NIST speaker recognition task.
Reinforcement Learning as Classification: Leveraging Modern Classifiers
 in Proceedings of the Twentieth International Conference on Machine Learning
, 2003
"... The basic tools of machine learning appear in the inner loop of most reinforcement learning algorithms, typically in the form of Monte Carlo methods or function approximation techniques. ..."
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Cited by 84 (5 self)
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The basic tools of machine learning appear in the inner loop of most reinforcement learning algorithms, typically in the form of Monte Carlo methods or function approximation techniques.
Mining Newsgroups Using Networks Arising From Social Behavior
, 2003
"... Recent advances in information retrieval over hyperlinked corpora have convincingly demonstrated that links carry less noisy information than text. We investigate the feasibility of applying linkbased methods in new applications domains. The specific application we consider is to partition authors ..."
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Cited by 78 (0 self)
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Recent advances in information retrieval over hyperlinked corpora have convincingly demonstrated that links carry less noisy information than text. We investigate the feasibility of applying linkbased methods in new applications domains. The specific application we consider is to partition authors into opposite camps within a given topic in the context of newsgroups. A typical newsgroup posting consists of one or more quoted lines from another posting followed by the opinion of the author. This social behavior gives rise to a network in which the vertices are individuals and the links represent "respondedto" relationships. An interesting characteristic of many newsgroups is that people more frequently respond to a message when they disagree than when they agree. This behavior is in sharp contrast to the WWW link graph, where linkage is an indicator of agreement or common interest. By analyzing the graph structure of the responses, we are able to effectively classify people into opposite camps. In contrast, methods based on statistical analysis of text yield low accuracy on such datasets because the vocabulary used by the two sides tends to be largely identical, and many newsgroup postings consist of relatively few words of text.
Classifying Large Data Sets Using SVM with Hierarchical Clusters
 in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, 2003
"... Support vector machine (SVM) has been a promising method for classification and regression analysis because of its solid mathematical foundation which conveys several salient properties that other methods do not provide. However, despite the prominent properties of SVM, it is not as favored for larg ..."
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Cited by 71 (3 self)
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Support vector machine (SVM) has been a promising method for classification and regression analysis because of its solid mathematical foundation which conveys several salient properties that other methods do not provide. However, despite the prominent properties of SVM, it is not as favored for largescale data mining as for pattern recognition or machine learning because the training complexity of SVM is highly dependent on the size of a data set. Many realworld data mining applications involve millions or billions of data records where even multiple scans of the entire data are too expensive to perform. This paper presents a new method, ClusteringBased SVM (CBSVM), which is specifically designed for handling very large data sets. CBSVM applies a hierarchical microclustering algorithm that scans the entire data set only once to provide an SVM with high quality samples that carry the statistical summaries of the data such that the summaries maximize the benefit of learning the SVM. CBSVM tries to generate the best SVM boundary for very large data sets given limited amount of resources. Our experiments on synthetic and real data sets show that CBSVM is highly scalable for very large data sets while also generating high classification accuracy.
Dynamic TimeAlignment Kernel in Support Vector Machine
, 2001
"... A new class of Support Vector Machine (SVM) that is applicable to sequentialpattern recognition such as speech recognition is developed by incorporating an idea of nonlinear time alignment into the kernel function. Since the timealignment operation of sequential pattern is embedded in the new ..."
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Cited by 69 (0 self)
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A new class of Support Vector Machine (SVM) that is applicable to sequentialpattern recognition such as speech recognition is developed by incorporating an idea of nonlinear time alignment into the kernel function. Since the timealignment operation of sequential pattern is embedded in the new kernel function, standard SVM training and classification algorithms can be employed without further modifications. The proposed SVM (DTAKSVM) is evaluated in speakerdependent speech recognition experiments of handsegmented phoneme recognition. Preliminary experimental results show comparable recognition performance with hidden Markov models (HMMs).
Exact simplification of support vector solutions
 Journal of Machine Learning Research
, 2001
"... This paper demonstrates that standard algorithms for training support vector machines generally produce solutions with a greater number of support vectors than are strictly necessary. An algorithm is presented that allows unnecessary support vectors to be recognized and eliminated while leaving the ..."
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Cited by 66 (0 self)
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This paper demonstrates that standard algorithms for training support vector machines generally produce solutions with a greater number of support vectors than are strictly necessary. An algorithm is presented that allows unnecessary support vectors to be recognized and eliminated while leaving the solution otherwise unchanged. The algorithm is applied to a variety of benchmark data sets (for both classification and regression) and in most cases the procedure leads to a reduction in the number of support vectors. In some cases the reduction is substantial.
Thin Junction Trees
 Advances in Neural Information Processing Systems 14
, 2001
"... We present an algorithm that induces a class of models with thin junction treesmodels that are characterized by an upper bound on the size of the maximal cliques of their triangulated graph. By ensuring that the junction tree is thin, inference in our models remains tractable throughout the l ..."
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Cited by 61 (2 self)
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We present an algorithm that induces a class of models with thin junction treesmodels that are characterized by an upper bound on the size of the maximal cliques of their triangulated graph. By ensuring that the junction tree is thin, inference in our models remains tractable throughout the learning process. This allows both an efficient implementation of an iterative scaling parameter estimation algorithm and also ensures that inference can be performed efficiently with the final model. We illustrate the approach with applications in handwritten digit recognition and DNA splice site detection.
Interior point methods for massive support vector machines
, 2003
"... We investigate the use of interiorpoint methods for solving quadratic programming problems with a small number of linear constraints, where the quadratic term consists of a lowrank update to a positive semidefinite matrix. Several formulations of the support vector machine fit into this category ..."
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Cited by 56 (1 self)
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We investigate the use of interiorpoint methods for solving quadratic programming problems with a small number of linear constraints, where the quadratic term consists of a lowrank update to a positive semidefinite matrix. Several formulations of the support vector machine fit into this category. An interesting feature of these particular problems is the volume of data, which can lead to quadratic programs with between 10 and 100 million variables and, if written explicitly, a dense Q matrix. Our code is based on OOQP, an objectoriented interiorpoint code, with the linear algebra specialized for the support vector machine application. For the targeted massive problems, all of the data is stored out of core and we overlap computation and input/output to reduce overhead. Results are reported for several linear support vector machine formulations demonstrating that the method is reliable and scalable. Key words. support vector machine, interiorpoint method, linear algebra AMS subject classifications.