About
Kernel Methods and Structured Domains
Substantial recent work in machine learning has focused on the problem of dealing with inputs and outputs on more complex domains than are provided for in the classical regression/classification setting. Structured representations can give a more informative view of input domains, which is crucial for the development of successful learning algorithms: application areas include determining protein structure and protein-protein interaction; part-of-speech tagging; the organization of web documents into hierarchies; and image segmentation. Likewise, a major research direction is in the use of structured output representations, which have been applied in a broad range of areas including several of the foregoing examples (for instance, the output required of the learning algorithm may be a probabilistic model, a graph, or a ranking).
Large Scale Kernel Machines
Datasets with millions of observations can be gathered by crawling the web, mining business databases, or connecting a cheap video tuner to a laptop. Vastly more ambitious learning systems are theoretically possible. The literature shows no shortage of ideas for sophisticated statistical models. The computational cost of learning algorithms is now the bottleneck. During the last decade, dataset size has outgrown processor speed. Meanwhile, machine learning algorithms became more principled, and also more computationally expensive.
Videos
Lectures

Large-scale parallel implementations of SVMs
Feb 25, 2007
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4812 views

Implementing SVM in an RDBMS: Improved Scalability and Usability
Feb 25, 2007
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4666 views

Learning Rankings for Information Retrieval
Feb 25, 2007
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8335 views

Extensions of Gaussian Processes for Ranking: Semi-Supervised and Active Learnin...
Feb 25, 2007
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4774 views

Large Scale Genomic Sequence Support Vector Machines
Feb 25, 2007
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4344 views

Kernels in Bioinformatics
Feb 25, 2007
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7193 views

Ranking as Learning Structured Outputs
Feb 25, 2007
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5799 views

Improved Fast Gauss Transform
Feb 25, 2007
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5380 views

The Pyramid Match Kernel: Efficient Learning with Sets of Features
Feb 25, 2007
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13214 views

Working Set Selection Using the Second Order Information for SVMs
Feb 25, 2007
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5815 views

Online Learning with a Memory Harness
Feb 25, 2007
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3256 views

Spectral Clustering and Transductive Inference for Graph Data
Feb 25, 2007
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5093 views

Object Correspondence as a Machine Learning Problem
Feb 25, 2007
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5811 views

An SMO-like algorithm for Kernel Conditional Random Fields
Feb 25, 2007
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6475 views

Learning from Network Traffic: Computing Kernels over Connection Content
Feb 25, 2007
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4600 views

Exploiting Hyperlinks to Learn a Retrieval Model
Feb 25, 2007
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3209 views