An Introduction to Support Vector Machines and Other Kernel-based Learning Methods John Shawe-Taylor, Nello Cristianini
Publisher: Cambridge University Press

Over 170,000 fever-related articles from PubMed abstracts and titles were retrieved and analysed at the sentence level using natural language processing techniques to identify genes and vaccines (including 186 Vaccine Ontology terms) as well as their interactions . Bpnn.py - Written by Neil Schemenauer, bpnn.py is used by an IBM article entitled "An introduction to neural networks". Their reproducibility was evaluated by an internal cross-validation method. It just struck me as an odd coincidence. Shogun - The machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM) . Learning with kernels support vector machines, regularization, optimization, and beyond. PyML focuses on SVMs and other kernel methods. "An Introduction to Support Vector Machines and Other Kernel-based Learning Methods". The method is based on analysis of the highly dynamic expression pattern of the eve gene, which is visualized in each embryo, and standardization of these expression patterns against a small training set of embryos with a known developmental age. It is supported on Linux Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. Support Vector Machines (SVM) [19] with an edit distance-based kernel function among these dependency paths [17] was used to classify whether a path describes an interaction between a gene or a gene-vaccine pair. We use the support vector regression (SVR) method ..