Computational cost of classification is as important as accuracy in on-line classification systems. The computational cost is usually dominated by the cost of computing implicit features of the raw input data. Very few efforts have been made to design classifiers which perform effectively with limited computational power; instead, feature selection is usually employed as a pre-processing step t...

We present a new algorithm for computing a SAGBI basis up to an arbitrary degree for a subalgebra generated by a set of homogeneous polynomials. Our idea is based on linear algebra methods which cause a low level of complexity and computational cost. We then use it to solve the membership problem in subalgebras.

In response to socioeconomic development, the number of machine learning applications has increased, along with calls for algorithmic transparency and further sustainability in terms energy efficient technologies. Modern computer algorithms that process large amounts information, particularly artificial intelligence methods their workhorse learning, can be used promote support sustainability; h...

Sample Entropy is the most popular definition of entropy and is widely used as a measure of the regularity/complexity of a time series. On the other hand, it is a computationally expensive method which may require a large amount of time when used in long series or with a large number of signals. The computationally intensive part is the similarity check between points in m dimensional space. In...

Information about the computational cost of programs is potentially useful for a variety of purposes, including selecting among diierent algorithms , guiding program transformations, in granularity control and mapping decisions in parallelizing compilers, and query optimization in deductive databases. Cost analysis of logic programs is complicated by nondeterminism: on the one hand, procedures ...

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

Abstract Many approaches for conducting Bayesian inference on discretely observed diffusions involve imputing diffusion bridges between observations. This can be computationally challenging in settings which the temporal horizon subsequent observations is large, due to poor scaling of algorithms simulating as observation distance increases. It common practical use a blocking scheme , path split...

Recently genetic studies have been revolutionized by next generation sequencing (NGS) technology, and it is expected that the use of this technology will largely eliminate defects in the methods of association studies. The NGS technology is becoming the premier tool in genetics. However, at the moment the use of this method is limited especially in the livestock due to high cost and computation...