Our new term extraction service analyzes text and an optional query, returning a list of the key concepts from the text. You can use the service for a variety of different purposes. For example, Y!Q uses it to determine key concepts within the search context and then uses those terms for augmenting a user's search query.
So it’s pretty clear by now that statistics and machine learning aren’t very different fields. I was recently pointed to a very amusing comparison by the excellent statistician — and machine learning expert — Robert Tibshiriani. Reproduced here: Glossary Machine learning Statistics network, graphs model weights parameters learning fitting generalization test set performance supervised learning regression/classification unsupervised learning density estimation, clustering large grant = $1,000,000 large grant = $50,000 nice place to have a meeting: Snowbird, Utah, French Alps nice place to have a meeting:Las Vegas in August
Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets. At the present time, Pig's infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, for which large-scale parallel implementations already exist Pig's language layer currently consists of a textual language called Pig Latin, which has the following key properties: * Ease of programming. It is trivial to achieve parallel execution of simple, "embarrassingly parallel" data analysis tasks. * Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically * Extensibility.