Practical Learning Algorithms for Structured Prediction

Machine learning techniques have been widely applied in many areas. In many cases, high accuracy requires training on large amount of data, adding more expressive features and/or exploring complex input and output structures, often resulting in scalability problems. Nevertheless, we observed that by carefully selecting and caching samples, structures, or latent items, we can reduce the problem size and improve the training speed and eventually improve the performance. Based on this observation, we developed efficient learning algorithms for structured prediction models. We showed that our approaches are able to learn expressive models from large amounts of annotated data and achieve state-of-the art performance on several natural language processing tasks.

发言人详细信息

Kai-Wei Chang is a doctoral candidate advised by Prof. Dan Roth in the Department of Computer Science, University of Illinois at Urbana-Champaign. His research interests lie in designing practical machine learning techniques for large and complex data and applying them to real world applications. He has been working on various topics in Machine learning and Natural Language Processing, including large-scale learning, structured learning, coreference resolution, and relation extraction. Kai-Wei was awarded the KDD Best Paper Award in 2010 and won the Yahoo! Key Scientific Challenges Award in 2011. He was one of the main contributors of a popular linear classification library, LIBLINEAR.

日期：
演讲者：
Kai-Wei Chang
所属机构：
University of Illinois at Urbana-Champaign

接下来观看