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Sparsity in Machine Learning and Statistics
Sparsity in Machine Learning and Statistics

Sparse estimation (or sparse recovery) is playing an increasingly important role in the statistics and machine learning communities. Several methods ...

Learning and Inference in Computational and Systems Biology
Learning and Inference in Computational and Systems Biology

In making advances within Computational Systems Biology there is an acknowledged need for the ongoing development of both probabilistic and ...

Summer Schools in Logic and Learning
Summer Schools in Logic and Learning

The Summer Schools in Logic and Learning bring together two annual summer schools in the area of logic and machine ...

NIPS ´08 Workshop: Kernel Learning - Automatic Selection of Optimal Kernels
NIPS ´08 Workshop: Kernel Learning - Automatic Selection of Optimal Kernels

Kernel methods are widely used to address a variety of learning tasks including classification, regression, ranking, clustering, and dimensionality reduction. ...

NIPS ´08 Workshop: Learning from Multiple Sources
NIPS ´08 Workshop: Learning from Multiple Sources

While the machine learning community has primarily focused on analysing the output of a single data source, there has been ...

NIPS ´08 Workshop: Optimization for Machine Learning
NIPS ´08 Workshop: Optimization for Machine Learning

Classical optimization techniques have found widespread use in machine learning. Convex optimization has occupied the center-stage and significant effort continues ...

NIPS ´08 Workshop: New Challenges in Theoretical Machine Learning: Learning with Data-dependent Concept Spaces
NIPS ´08 Workshop: New Challenges in Theoretical Machine Learning: Learning with Data-dependent Concept Spaces

This workshop aims at collecting theoretical insights in the design of data-dependent learning strategies. Specifically we are interested in how ...

NIPS ´08 Workshop: Causality: objectives and assessment
NIPS ´08 Workshop: Causality: objectives and assessment

Machine learning has traditionally been focused on prediction. Given observations that have been generated by an unknown stochastic dependency, the ...

NIPS ´08 Workshop: Machine Learning in Computational Biology
NIPS ´08 Workshop: Machine Learning in Computational Biology

The field of computational biology has seen dramatic growth over the past few years, both in terms of new available ...

NIPS ´08 Workshop: Machine Learning Open Source Software
NIPS ´08 Workshop: Machine Learning Open Source Software

We believe that the wide-spread adoption of open source software policies will have a tremendous impact on the field of ...

NIPS ´08 Workshop: Structured Input - Structured Output
NIPS ´08 Workshop: Structured Input - Structured Output

Structured data emerges rapidly in a large number of disciplines: bioinformatics, systems biology, social network analysis, natural language processing and ...

NIPS ´08 Workshop: Algebraic and combinatorial methods in machine learning
NIPS ´08 Workshop: Algebraic and combinatorial methods in machine learning

There has recently been a surge of interest in algebraic methods in machine learning. In no particular order, this includes: ...

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Walter H. G. Lewin 36059 views
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Project summary - PASCAL NoE

PASCAL PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The objective is to build a Europe-wide Distributed Institute which will pioneer principled methods of pattern analysis, statistical modelling and computational learning as core enabling technologies for multimodal interfaces that are capable of natural and seamless interaction with and among individual human users.
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SearchPoint is about searching in a new way

<img src="http://searchpoint.ijs.si/images/logo.jpg" alt="SearchPoint"/> When we search for something, we are very used to the following: We write a query and a search engine returns a sorted list of pages. Since this works pretty well, SearchPoint also offers this basic functionality. So what is new? Well we are of a firm belief that for one query many different rankings should be ...

WORKSHOPS:

NIPS ´08 Workshop: Kernel Learning - Automatic Selection of Optimal Kernels
Kernel methods are widely used to address a variety of learning tasks including classification, regression, ranking, clustering, and dimensionality reduction. ...
NIPS ´08 Workshop: Learning from Multiple Sources
While the machine learning community has primarily focused on analysing the output of a single data source, there has been ...
NIPS ´08 Workshop: Optimization for Machine Learning
Classical optimization techniques have found widespread use in machine learning. Convex optimization has occupied the center-stage and significant effort continues ...

SCHOOLS:

Summer Schools in Logic and Learning

The Summer Schools in Logic and Learning bring together two annual summer schools in the area of logic and machine ...

AERFAI Summer School on New Trends in Pattern Recognition for Language Technologies

The aim of this Summer School is to introduce PhD students and young researchers into challenging areas of Natural Language ...

Machine Learning Summer School 2008 - Kioloa

This school is suitable for all levels, both for people without previous knowledge in Machine Learning, and those wishing to ...

EPSRC Winter School in Mathematics for Data Modelling

This Winter School aims to bring together researchers who have an interest in data modelling across a broad range of ...

Machine Learning Summer School 2007 - Tuebingen

Machine Learning is a foundational discipline of the Information Sciences. It combines theory from areas as diverse as Statistics, Mathematics, ...