Three hybrid data based model construction/pruning formula are introduced by using backward elimination as automatic postprocessing approaches to improved model sparsity. Each of these approaches is based on a composite cost function between the model fit and one of three terms of A-/D-optimality / (parameter 1-norm in basis pursuit) that determines a pruning process. The A-/D-optimality based ...

We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid feature selection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimal feature vector that well represents the shapes of the subjects in the images. In detail, the proposed feature selection algorithm adopts the k-fold subsampling and sequential ba...

Selection of a subset of vectors from a larger dictionary of vectors arises in a wide variety of application areas. This problem is known to be NP-hard and many algorithms have been proposed for the suboptimal solution of this problem. The focus of this paper is the development of a backward sequential elimination algorithm wherein, starting from the full dictionary, elements are deleted until ...

RTP2S merupakan program pencanangan Rumah Tangga Pelopor Pencegahan Stunting, pendekatanya diimplementasikan melalui model pilot yang didasarkan desa lokus stunting dan bermasalah sanitasi air bersih. Permaslahan terjadi yaitu pemerintah tidak ada data langsung dari masyarakat untuk mengetahui berhasil tidaknya atau puas masyarajkat dengan adanya telah dijalankan. Klasifikasi keberasilan dinila...

New feature selection algorithms for linear threshold functions are described which combine backward elimination with an adaptive regularization method. This makes them particularly suitable to the classification of microarray expression data, where the goal is to obtain accurate rules depending on few genes only. Our algorithms are fast and easy to implement, since they center on an incrementa...

Unraveling transcriptional regulatory networks is essential for understanding and predicting cellular responses in different developmental and environmental contexts. Information-theoretic methods of network inference have been shown to produce high-quality reconstructions because of their ability to infer both linear and non-linear dependencies between regulators and targets. In this paper, we...

BACKGROUND End Stage Renal Failure (ESRD) is the last stage of the chronic renal failure in which kidneys become completely fail to function. AIM The basic aim of this study was to discover the important risk factors of ESRD and to construct a model for prediction of the ESRD patients in various hospitals of Peshawar, Pakistan. MATERIAL AND METHODS The data were collected from the patients ...

Combining multiple neural networks appears to be a very promising approach in improving neural network generalisation since it is very difficult, if not impossible, to develop a perfect single neural network. In the building of an aggregated neural network model, a number of individual networks are developed from different data sets and/or different training algorithms. In this paper, individua...

The inverse method is a generic proof search procedure applicable to non-classical logics satisfying cut elimination and the subformula property. In this paper we describe a general architecture and several high-level optimizations that enable its efficient implementation. Some of these rely on logic-specific properties, such as polarization and focusing, which have been shown to hold in a wide...