Divide and Conquer Methods for Machine Learning

点击翻译按钮获取中文摘要

This project will develop machine learning algorithms, prototype tools, and supporting theory for solving complex machine learning problems. Existing theory and algorithms have focused on learning simple classifiers that take a description of an object and assign it to one of a small number of classes (e.g., taking an image of a character and classifying it as one of the 26 letters of the alphabet). Emerging applications in science and industry require learning much more complex functions that map from complex inputs (e.g., 2D maps, time series, and strings) to complex outputs (e.g., other 2D maps, time series, and strings). Despite the lack of theory covering such cases, many practical systems have been built that work well in particular applications. These systems all employ some form of divide-and-conquer, where the inputs and outputs are divided into smaller pieces ("windows"), classified, and then the results are merged to produce an overall solution. This project will develop a general formulation of machine learning for divide-and-conquer problems, a collection of algorithms for solving these problems, and a prototype tool kit for solving new learning problems via the divide-and-conquer approach. In addition, theoretical models will be developed to understand the tradeoffs that affect the design of divide-and-conquer systems. The resulting algorithms and theory will extend the range of problems that can be solved via machine learning methods and make it easier to construct new divide-and-conquer machine learning applications. This will lead to improved performance of existing machine learning applications, for example, in text processing, intrusion detection, and the analysis of sensor data to signal alarms.