Lasso regression tends to assign zero weights to most irrelevant or redundant features, and hence is a promising technique for feature selection. Its limitation, however, is that it only offers solutions to linear models. Kernel machines with feature scaling techniques have been studied for feature selection with non-linear models. However, such approaches require to solve hard non-convex optim...

This paper presents a fast and novel method to speed up training and evaluation of support vector machine (SVM) classifiers with a very large set of linear features. A pre-computation step and a redefinition of the kernel function handle linear feature evaluation implicitly and thus result in a run-time complexity as if no linear features were evaluated at all. We then train a classifier for fa...

The performance of NLP classifiers largely depends on the quality of the features considered for prediction (feature engineering). However, as the number of features increases, the more likely overfitting becomes and performance decreases. Also, due to the very large number of features, only slimple linear classifiers are considered, thus disregarding potentially predictive non-linear combinati...

In this paper, a new method is presented to extract robust speech features in the presence of the external noise. The proposed method based on two-dimensional Gabor filters takes in account the spectro-temporal modulation frequencies and also limits the redundancy on the feature level. The performance of the proposed feature extraction method was evaluated on isolated speech words which are ext...

Physical characteristics of different features in desert is a reflection of severe thermal and climatic conditions. In this paper, diurnal surface temperature patterns of important surface features in Lut Desert were studied and the relationship among different surfaces analyzed. Diurnal trend in surface temperature of surface types, marl, dark sand, light sand, salt-affected soil, soul at 10 c...

A novel method is proposed for combining multiple probabilistic classifiers on different feature sets. In order to achieve the improved classification performance, a generalized finite mixture model is proposed as a linear combination scheme and implemented based on radial basis function networks. In the linear combination scheme, soft competition on different feature sets is adopted as an auto...

Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern have a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to reduce the cost of feature measurement, inc...