Compared with linear kernels, tree kernels can effectively explore implicitly huge syntactic structured features embedded in a parse tree.
The lite version takes around 620 Mbytes of disk space including a 2.6.0 kernel source tree.
To apply this algorithm to mobile robots equipped with range finders, a kernel density tree is learned that permits fast sampling.
Among the popular machine learning approaches to extracting PPIs from biomedical literature, kernel-based methods including tree kernels [ 16], shortest path kernels [ 17], and graph kernels [ 18] have been proposed for PPIs extraction.
Features are tested for statistical significance (wilcoxon rank) before use in the classifier SVM with RBF kernel, boosted trees, random forests, naive bayes 715 images from PPMI database (208 HC, 427 PD, 80 scans without evidence of dopaminergic deficit (SWEDD)).
Subsequently, 9 distinct types of predictive models are fitted using the reduced data set: logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs; using linear, radial basis function and polynomial kernels), decision tree (DT), random forest (RF), and stochastic gradient boosting (SGB).
The reduced data set of 24 parameters is fitted using 9 distinct types of predictive models: logistic regression, linear discriminant analysis, quadratic discriminant analysis, support vector machines (using linear, radial basis function and polynomial kernels), decision tree, random forest and stochastic gradient boosting.
DP techniques similar to ours are used there for computing kernels between trees (Collins and Duffy, 2001; Kuboyama, 2007).
(iii) We can use domain-specific kernels (e.g. graph and tree kernels for structured objects) to better capture the underlying biological processes (Schölkopf et al., 2004).
