Читать книгу Informatics and Machine Learning - Stephen Winters-Hilt - Страница 21

All of the core methods described thus far (FSA, HMM, SVM) require some amount of parameter “tuning” for good performance. In essence, tuning is a search through parameter space (of the method) for best performance (according to a variety of metrics). The tuning on acquisition parameters in an FSA, or choice of states in a HMM, or SVM Kernels and Kernel parameters, is often not terribly complicated allowing for a “brute‐force” search over a set of parameters, choosing the best from that set. On occasion, however, a more elaborate, and fully automated, search‐optimization is sought (or just search problem in general), For more complex search tasks it is good to know the modern search methodologies and what they are capable of, so these are described in Chapter 11.