predictSVM

predictSVM,ExpicitRepresentation-method

predictSVM,ExplicitRepresentation-method

predictSVM,KernelMatrix-method

predictSVM,missing-method

predictSVM.KernelMatrix

trainSVM

trainSVM,ExplicitRepresentation-method

trainSVM,KernelMatrix-method

model

predictionType

verbose

additional arguments to be passed to the selected SVM

svmInfo

Functions for SVM access (used only for testing purpose)

The package provides functionality for kernel-based analysis of
        DNA, RNA, and amino acid sequences via SVM-based methods. As core
        functionality, kebabs implements following sequence kernels:
        spectrum kernel, mismatch kernel, gappy pair kernel, and
        motif kernel. Apart from an efficient implementation of standard
        position-independent functionality, the kernels are extended in a
        novel way to take the position of patterns into account for the
        similarity measure. Because of the flexibility of the kernel
        formulation, other kernels like the weighted degree kernel or
        the shifted weighted degree kernel with constant weighting of
        positions are included as special cases. An annotation-specific
        variant of the kernels uses annotation information placed along
        the sequence together with the patterns in the sequence.
        The package allows for the generation of a kernel matrix or an
        explicit feature representation in dense or sparse format for all
        available kernels which can be used with methods implemented in
        other R packages. With focus on SVM-based methods, kebabs
        provides a framework which simplifies the usage of existing
        SVM implementations in kernlab, e1071, and LiblineaR. Binary and
        multi-class classification as well as regression tasks can be used
        in a unified way without having to deal with the different
        functions, parameters, and formats of the selected SVM. As support
        for choosing hyperparameters, the package provides cross
        validation - including grouped cross validation, grid search and
        model selection functions. For easier biological interpretation of
        the results, the package computes feature weights for all SVMs and
        prediction profiles which show the contribution of individual
        sequence positions to the prediction result and indicate the
        relevance of sequence sections for the learning result and the
        underlying biological functions.

predictSVM: SVM Access for Training and Prediction

Description

Usage

Arguments

Value

Details

Examples