是一个支持向量机模型，可以实现复杂的（多元）Ÿ输出数据集，如树木，序列，或。 These complex output SVM models can be applied to natural language parsing, sequence alignment in protein homology detection, and Markov models for part-of-speech tagging. 这些复杂的输出支持向量机模型可以应用于自然语言解析，同源序列比对发现的蛋白质，和马尔可夫模型部分的语音标记。 Several implementations exist: SVMmulticlass, for multi-class classification; SVMcfg, learns a weighted context free grammar from examples; SVMalign, learns to align protein sequences from training alignments; SVMhmm, learns a Markov model from examples. 几个实现存在：SVMmulticlass，多级分类; SVMcfg，学会从上下文无关文法的例子加权; SVMalign，学会调整训练路线蛋白质序列，SVMhmm，学习模式，从一个马尔可夫例子。 These modules have straightforward applications in bioinformatics, but one can imagine significant implementations for cheminformatics, when the chemical structure is represented as trees or sequences. 这些模块在生物信息学简单的应用，但是人们可以想像或序列的显着实现对化学信息学，化学时表示为树结构。