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Introduction to Semi-Supervised Learning
This chapter contains sections titled: Supervised, Unsupervised, and Semi-Supervised Learning, When Can Semi-Supervised Learning Work?, Classes of Algorithms and Organization of This Book
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Semi-Supervised Classification by Low Density Separation
We believe that the cluster assumption is key to successful semi-supervised learning. Based on this, we propose three semi-supervised algorithms: 1. deriving graph-based distances that emphazise lowExpand
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lp-Norm Multiple Kernel Learning
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Efficient and Accurate Lp-Norm Multiple Kernel Learning
Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel learning (MKL) promote sparse kernelExpand
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l p -Norm Multiple Kernel Learning
Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel learning (MKL) promote sparse kernelExpand
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Semi-Supervised Learning
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (inExpand
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Multiclass multiple kernel learning
In many applications it is desirable to learn from several kernels. "Multiple kernel learning" (MKL) allows the practitioner to optimize over linear combinations of kernels. By enforcing sparseExpand
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Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites
MOTIVATION In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are calledExpand
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A continuation method for semi-supervised SVMs
Semi-Supervised Support Vector Machines (S3VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do not cut clusters.Expand
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ARTS: accurate recognition of transcription starts in human
UNLABELLED We develop new methods for finding transcription start sites (TSS) of RNA Polymerase II binding genes in genomic DNA sequences. Employing Support Vector Machines with advanced sequenceExpand
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