PSoL is an iterative method developed by Wang, et al. [ 16] to predict ncRNA genes from the E. coli genome and to define an optimized negative class using two-class SVM.
Lately, Wang, et al. [ 16] developed an elegant algorithm, positive sample only learning (PSoL), to predict non-coding RNA (ncRNA) genes by generating an optimized negative class of ncRNA from so-called "unlabeled" data using two-class SVM.
In phase III, the developed statistical models were used to derive optimized industrial class ESH LWSCCs.
In the last section, the proposed method is used for designing of the optimized new class of lens based on the physical concept of gradient refractive index material, and radar cross section reduction based on gradual wave absorption.
While PCA avoids the problem of overfitting by taking an unsupervised approach to pattern recognition, it is not optimized for class separation.
We propose to apply optimized one-class support vector machines (1-SVMs) to tackle both sound detection and classification tasks in the sound recognition process.
The paper authored by A. Rabaoui et al. applies optimized one-class support vector machines (1-SVMs) to tackle sound detection as well as classification tasks in the context of a sound recognition process.
However, because more than three tissue types are often differentiated during pathological diagnosis, a stable or optimized multi-class predictor is needed.
We examined various combinations of binary classifiers to perform optimized multi-class prediction, and applied our system to gene expression data from four tissue types of human thyroid origin.
Moreover, their work provides an outline for inductive coils and optimized parameters of class E amplifiers.
