View order

Type of content







...Search a Keyword

event header image

NIPS Workshop 2008 - Whistler   

NIPS Workshop on Learning from Multiple Sources, Whistler 2008

While the machine learning community has primarily focused on analysing the output of a single data source, there has been relatively few attempts to develop a general framework, or heuristics, for analysing several data sources in terms of a shared dependency structure. Learning from multiple data sources (or alternatively, the data fusion problem) is a timely research area. Due to the increasing availability and sophistication of data recording techniques and advances in data analysis algorithms, there exists many scenarios in which it is necessary to model multiple, related data sources, i.e. in fields such as bioinformatics, multi-modal signal processing, information retrieval, sensor networks etc.

The open question is to find approaches to analyse data which consists of more than one set of observations (or view) of the same phenomenon. In general, existing methods use a discriminative approach, where a set of features for each data set is found in order to explicitly optimise some dependency criterion. However, a discriminative approach may result in an ad hoc algorithm, require regularisation to ensure erroneous shared features are not discovered, and it is difficult to incorporate prior knowledge about the shared information. A possible solution is to overcome these problems is a generative probabilistic approach, which models each data stream as a sum of a shared component and a private component that models the within-set variation.

In practice, related data sources may exhibit complex co-variation (for instance, audio and visual streams related to the same video) and therefore it is necessary to develop models that impose structured variation within and between data sources, rather than assuming a so-called 'flat' data structure. Additional methodological challenges include determining what is the 'useful' information to extract from the multiple data sources, and building models for predicting one data source given the others. Finally, as well as learning from multiple data sources in an unsupervised manner, there is the closely related problem of multitask learning, or transfer learning where a task is learned from other related tasks.

More information about workshop -

Reviews and comments:

Comment1 Mukesh Hamirani, August 7, 2009 at 8:40 a.m.:

It is good plan for vedio lectures,i hope you add more good lectures in this site. 1st time i havn't knowledge to speak, listen and learnt english language but some friends said that use this site u will success. When i was try that time i had zero but now iam fit to write and listening correctly english language. I hope & pray for yours planning. Thnx

Mukesh Hamirani
World's Star Tech Mithi Tharparkar.

Write your own review or comment:

make sure you have javascript enabled or clear this field: