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Object detection problem is traditionally tackled as two class problem. Wherein the non object classes are not precisely defined. In this paper we propose cascade of principal component modeling with associated test statistics and reduced set support vector data description for efficient object detection, both of which hinge mainly on modeling of object class training data. The PCA modeling enables quick rejection of comparatively obvious non object in initial stage of the cascade to gain computation advantage. The reduced set SVDD is applied in latter stages of cascade to classify relatively difficult images. This combination of PCA modeling and reduced set support vector data description leads to a good object detection with simple pixel features.

In this paper we present a novel approach for nonlinear time series prediction using Kernel methods. The kernel methods such as Support Vector Machine(SVM) and Support Vector Regression(SVR) deal with nonlinear problems assuming independent and identically distributed (i.i.d.) data, without explicit notion of time. However, the problem of prediction necessitates temporal information. In this regard, we propose a novel time series modeling technique, Kernel Auto-Regressive model with eXogenous inputs (KARX) and associated estimation methods. Amongst others the advantage of KARX model compared to the widely used Nonlinear Auto-Regressive eXogenous (NARX) model (which is implemented using Artificial Neural Network (ANN)) is, implicit nonlinear mapping and better regularization capability. In this work, we make use of Kalman recursions instead of quadratic programming which is generally used in kernel methods. Also, we employ online estimation schemes for estimating model noise parameters. The efficacy of the approach is demonstrated on artificial time series as well as real world time series acquired from aircraft engines.

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