Statistical methods for speech recognition
Statistical methods for speech recognition
Learning to Recognize Volcanoes on Venus
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
A Machine Learning Approach to POS Tagging
Machine Learning
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Connectionist Speech Recognition: A Hybrid Approach
Connectionist Speech Recognition: A Hybrid Approach
Data Mining and Knowledge Discovery
Alignment algorithms for learning to read aloud
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Divide and Conquer Machine Learning for a Genomics Analogy Problem (Progress Report)
DS '01 Proceedings of the 4th International Conference on Discovery Science
Hi-index | 0.00 |
Existing machine learning theory and algorithms have focused on learning an unknown function from training examples, where the unknown function maps from a feature vector to one of a small number of classes. Emerging applications in science and industry require learning much more complex functions that map from complex input spaces (e.g., 2-dimensional maps, time series, and strings) to complex output spaces (e.g., other 2-dimensional 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 (e.g., "windows"), classified, and then the results are merged to produce an overall solution. This paper defines the problem of divide-and-conquer learning and identifies the key research questions that need to be studied in order to develop practical, general-purpose learning algorithms for divide-and-conquer problems and an associated theory.