Identifying significant genes among thousands of sequences on a microarray is a central challenge for cancer research in bioinformatics. A multitude of methods have been proposed for this task of feature selection, yet the selected gene lists differ greatly between different methods. To accomplish biologically meaningful gene selection from microarray data, we have to understand the theoretical...

We consider a k-sample problem, k > 2, where samples have been obtained from k (random) generators, and we are interested in identifying those samples, if any, that exhibit substantial deviations from a pattern given by most of the samples. This main pattern would consist of component samples which should exhibit some internal degree of similarity. To handle similarity, can be of interest in a ...

We present a variation of the genetic programming algorithm, called Historically Assessed Hardness (HAH), in which the fitness rewards for particular test cases are scaled in proportion to their relative difficulty as gauged by historical solution rates. The method is similar in many respects to some realizations of techniques such as implicit fitness sharing, stepwise adaptation of weights and...

The development of a new general radix-b division algorithm, based on the Svoboda-Tung division, suitable for VLSI implementation is presented. The new algorithm overcomes the drawbacks of the Svoboda-Tung techniques that have prevented the VLSI implementation. First of all, the proposed algorithm is valid for any radix b ≥ 2; and next, it avoids the possible compensation due to overflow on the...

As machine learning algorithms enter applications in industrial settings, there is increased interest in controlling their cpu-time during testing. The cpu-time consists of the running time of the algorithm and the extraction time of the features. The latter can vary drastically when the feature set is diverse. In this paper, we propose an algorithm, the Greedy Miser, that incorporates the feat...

In modern computers, the floating point unit is the part of the processor delivering the highest computing power and getting most attention from the design team. Performance of any multiple precision application will be dramatically enhanced by adequate use of floating point expansions. We present in this work three multiplication algorithms faster and more integrated than the stepwise algorith...

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...

This paper presents a systematic methodology based on the algebraic theory of signal processing to classify and derive fast algorithms for linear transforms. Instead of manipulating the entries of transform matrices, our approach derives the algorithms by stepwise decomposition of the associated signal models, or polynomial algebras. This decomposition is based on two generic methods or algebra...

This paper presents a B-spline based path planning approach for agricultural guidance applications which is able to handle successively generated trajectories. Additionally, the lateral controller which was used to calculate the steering angle is described and the method by which the required parameters were determined is introduced. We explain stepwise how the output of a perception system is ...

Our understanding of the program derivation process has evolved to the point where it can be described in terms of a clearly defined sequence of steps. In this paper, we will identify those steps and show how they may be used to derive programs from formal specifications. In describing the program derivation process we will focus on two things, its broad structure, and some detail for each of t...