Multiple-kernel learning methods use multiple kernels by combining them into a single one via a combination function.
In this paper, we propose a new strategy for behaviour acquisition for domestic robots where the behaviours are acquired using multiple differing learning methods that are subsequently incorporated into a common behaviour selection system, enabling them to be performed in appropriate situations.
Based on the above considerations, this study aimed to compare the performance of multiple machine learning methods for classifying mediastinal lymph node NSCLC from PET/CT images.
Multi-label multiple kernel learning methods often learn a composite kernel for each binary label and thus have a complexity linear to the number of labels.
In addition, there have been a series of multiple kernel learning methods designed for integrating heterogeneous genomic data (Alpaydin, 2011; Hamid et al., 2012; Yu et al., 2010).
In a third step, we applied multiple machine learning methods (see Methods) to filter false-positives to obtain three highly accurate, functional domain cluster (NB-ARC/LRR/TIR) (Additional file 2).
The adoption of present integrated module for Central Nervous System and the use of multiple teaching learning methods have been proven to be useful in acquisition of knowledge from the student satisfaction point of view.
The adoption of present integrated module for CNS and the use of multiple teaching learning methods have been proven to be useful in acquisition of knowledge from the student satisfaction point of view.
Babenko et al.[2] proposed a novel tracking method based on the online multiple instance learning method, where the current tracking position is considered uncertain and several positive samples are selected close to current object position, arranged in a so-called bag.
RegioSelectivity (RS -predictor is developed by ZaRS -predictor [15, 16], whish employs a set of 392 quantum chemical atom-specific andevelopedological descriptors, and a support vector machine (SVM)-like ranking in combynation with a multiple instance learning method to determine potential SOMs.
