Brain-Computer interface systems are a new mode of communication which provides a new path between brain and its surrounding by processing EEG signals measured in different mental states.  Therefore, choosing suitable features is demanded for a good BCI communication. In this regard, one of the points to be considered is feature vector dimensionality. We present a method of feature reduction us...

Parameter selection is one of the important steps involved in any model fitting. In this paper we have used Uniform Design Tables to choose the parameters for PSVM and SVM to classify the data. UD is one of the efficient space filling designs, which spreads the combination of parameters in the space uniformly scattered and generalizes the performance of the model efficiently. This paper compare...

Sensor drift is currently the most challenging problem in gas sensing. We propose a novel ensemble method with dynamic weights based on fitting (DWF) to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. The DWF method uses a dynamic weighted combination of support vector machine (SVM) classifiers trained by the datasets ...

Fitting sparse models to high-dimensional time series is an important area of statistical inference. In this paper, we consider vector autoregressive and develop appropriate bootstrap methods infer properties such processes. Our methodology generates pseudo using a model-based procedure which involves estimated, sparsified version the underlying model. Inference performed so-called de-sparsifie...

AAM has been successfully applied to face alignment, but its performance is very sensitive to initial values. In case the initial values are a little far distant from the global optimum values, there exists a pretty good possibility that AAM-based face alignment may converge to a local minimum. In this paper, we propose a progressive AAM-based face alignment algorithm which first finds the feat...

Photometric redshifts have been regarded as efficient and effective measures for studying the statistical properties of galaxies and their evolution. In this paper, we introduce SVM_Light, a freely available software package using support vector machines (SVM) for photometric redshift estimation. This technique shows its superiorities in accuracy and efficiency. It can be applied to huge volume...

We consider methods for reducing the effects of fitting nuisance parameters on a general estimating function, when the estimating function depends on not only a vector of parameters of interest, θ, but also a vector of nuisance parameters, λ. We propose a class of modified profile estimating functions with plug-in bias reduced by two orders. A robust version of the adjustment term does not requ...