None of the previous work in BioCreative I used semantic similarity methods, including vector- or graph-based methods.
Based on these pioneering works, aggregated vector-based methods emerged, including vector quantization [3], sparse coding [4], locality-constrained linear coding [5], and soft assignment [6].
Many of these methods, including support vector machines (SVMs), the main topic of this chapter, have been applied with success to information retrieval problems, particularly text classification.
The proposed method is compared with other methods, including support vector machine (SVM), neural network, and Bayesian classifier.
We compare the experimental results obtained by Genetic Programming with the ones of a set of "non-evolutionary" Machine Learning methods, including Support Vector Machines, Artificial Neural Networks, Linear and Least Square Regression.
In [110, 111, 112], local features such as histogram of oriented gradients (HOG) were processed with pattern recognition methods including support vector machine (SVM) and cascaded AdaBoost.
Further the efficiency of the feature selection methods including support vector machine recursive feature elimination (SVM-RFE), Chi Squared, and CSF were compared.
We compared ANMM4CBR with several typical classification methods, including support vector machine (SVM) [ 7] with linear kernal, k-nearest neighbor (kNN, we set k = 3), and LogitBoost [ 6].
Some reports have demonstrated that RF, as with other methods including support vector machines (SVMs) and linear discriminant analysis, outperforms in the classification of several cancers (Wu et al, 2003; Statnikov et al, 2008; Kim et al, 2009).
So far, most empirical studies have used logistic or Cox proportional hazards regression analyses [ 3], but other methods including support vector machine learning, fuzzy logic, neural networks and classification trees have been applied [ 80, 81].
