文件名称：Shogun 3.0
文件大小：4MB
文件格式：BZ2
更新时间：2016-12-11 17:11:34
机器学习
The machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM) [1]. It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art LibSVM [2] and SVMlight [3]. Each of the SVMs can be combined with a variety of kernels. The toolbox not only provides efficient implementations of the most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number of recent string kernels as e.g. the Locality Improved [4], Fischer [5], TOP [6], Spectrum [7], Weighted Degree Kernel (with shifts) [8, 9, 10]. For the latter the efficient LINADD [10] optimizations are implemented. Also SHOGUN offers the freedom of working with custom pre-computed kernels. One of its key features is the *combined kernel* which can be constructed by a weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain. An optimal sub-kernel weighting can be learned using Multiple Kernel Learning [11, 12, 16]. Currently SVM 2-class classification and regression problems can be dealt with. However SHOGUN also implements a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and features algorithms to train hidden markov models. The input feature-objects can be dense, sparse or strings, and of types int/short/double/char. In addition, they can be converted into different feature types. Chains of *preprocessors* (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing.