In this study six different mode switching techniques (i.e. timeout mode switching, non-preferred hand mode switching, barrel button mode switching, pressure mode switching, tilt mode switching and azimuth mode switch...
详细信息
ISBN:
(纸本)9780769534718
In this study six different mode switching techniques (i.e. timeout mode switching, non-preferred hand mode switching, barrel button mode switching, pressure mode switching, tilt mode switching and azimuth mode switching) based on multiple parameters pen input are proposed. The results indicate that the techniques utilizing tilt angle and azimuth offer faster performance than the others.
The main objective of this work is to automatically design neural network models with sigmoidal basis units for classification tasks, so that classifiers are obtained in the most balanced way possible in terms of CCR ...
详细信息
This paper presents a new algorithm that approximates real function evaluations using supervised learning with a surrogate method called support vector machine (SVM). We perform a comparative study among different lea...
详细信息
Three algorithms are proposed in optimizing the regression test suite when the test suite reduction technique and test case prioritization technique are combined together. 1) Build-up algorithm, which first selects th...
详细信息
Three algorithms are proposed in optimizing the regression test suite when the test suite reduction technique and test case prioritization technique are combined together. 1) Build-up algorithm, which first selects the essential test case and then the one with the biggest additional contribution until all the requirements are satisfied. 2) Break-down algorithm, which iteratively discards the redundant test case with the smallest contribution until all the test cases are essential or all the requirements are satisfied. 3) Test case prioritization strategy iteratively uses build-up algorithm until all the test cases are ordered. Each test-case contribution is not only related to the importance of each requirement but also to the whole requirements set. Experimental studies are performed to show that test-suite reduction combined with the test-suite prioritization technique can provide a smaller-sized test suite with a higher ARRS value.
The main objective of this work is to automatically design neural network models with sigmoidal basis units for classification tasks, so that classifiers are obtained in the most balanced way possible in terms of CCR ...
详细信息
The main objective of this work is to automatically design neural network models with sigmoidal basis units for classification tasks, so that classifiers are obtained in the most balanced way possible in terms of CCR and sensitivity (given by the lowest percentage of examples correctly predicted to belong to each class). We present a memetic Pareto evolutionary NSGA2 (MPENSGA2) approach based on the Pareto-NSGAII evolution (PNSGAII) algorithm. We propose to augmente it with a local search using the improved Rprop-IRprop algorithm for the prediction of growth/no growth of L. monocytogenes as a function of the storage temperature, pH, citric (CA) and ascorbic acid (AA). The results obtained show that the generalization ability can be more efficiently improved within a framework that is multi-objective instead of a within a single-objective one.
Performance evaluation is decisive when improving classifiers. Accuracy alone is insufficient because it cannot capture the myriad of contributing factors differentiating the performances of two different classifiers ...
详细信息
Performance evaluation is decisive when improving classifiers. Accuracy alone is insufficient because it cannot capture the myriad of contributing factors differentiating the performances of two different classifiers and approaches based on a multi-objective perspective are hindered by the growing of the Pareto optimal front as the number of classes increases. This paper proposes a new approach to deal with multi-class problems based on the accuracy (C) and minimum sensitivity (S) given by the lowest percentage of examples correctly predicted to belong to each class. From this perspective, we compare different fitness functions (accuracy, C , entropy, E , sensitivity, S , and area, A ) in an evolutionary scheme. We also present a two stage evolutionary algorithm with two sequential fitness functions, the entropy for the first step and the area for the second step. This methodology is applied to solve six benchmark classification problems. The two-stage approach obtains promising results and achieves a high classification rate level in the global dataset with an acceptable level of accuracy for each class.
Grid computing is an active field in high-availability computing. This paper focuses on the numerical calculation of two-dimensional frequency selective surface using the finite difference time domain (FDTD) method on...
详细信息
Grid computing is an active field in high-availability computing. This paper focuses on the numerical calculation of two-dimensional frequency selective surface using the finite difference time domain (FDTD) method on grid-enabled cluster. The result of experiment shows that the evaluation via the parallel FDTD is in good agreement with the result obtained via the traditional FDTD method. The parallel FDTD algorithm is correct and reasonable, which can be extended to other periodic structure and finite exciting sources.
In this study six different mode switching techniques (i.e. timeout mode switching, non-preferred hand mode switching, barrel button mode switching, pressure mode switching, tilt mode switching and azimuth mode switch...
详细信息
In this study six different mode switching techniques (i.e. timeout mode switching, non-preferred hand mode switching, barrel button mode switching, pressure mode switching, tilt mode switching and azimuth mode switching) based on multiple parameters pen input are proposed. The results indicate that the techniques utilizing tilt angle and azimuth offer faster performance than the others.
暂无评论