Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm

The main difficulty with EM algorithm for mixture model concerns the number of components, say g. This is the question of model selection, and the EM algorithm itself could not estimate g. On the contrary, the algorithm requires g to be specified before the remaining parameters can be estimated. To solve this problem, a new algorithm, which is called stepwise split-and-merge EM (SSMEM) algorithm, is proposed. The SSMEM algorithm alternately splits and merges components, estimating g and other parameters of components simultaneously. Also, two novel criteria are introduced to efficiently select the components for split or merge. Experimental results on simulated and real data demonstrate the effectivity of the proposed algorithm.     

Employing optimized combinations of one-class classifiers for automated currency validation

Automated currency validation requires a decision to be made regarding the authenticity of a banknote presented to the validation system. This decision often has to be made with little or no information regarding the characteristics of possible counterfeits as is the case for issues of new currency. A method for automated currency validation is presented which segments the whole banknote into different regions, builds individual classifiers on each region and then combines a small subset of the region specific classifiers to provide an overall decision. The segmentation and combination of region specific classifiers to provide optimized false positive and false negative rates is achieved by employing a genetic algorithm. Experiments based on high value notes of Sterling currency were carried out to assess the effectiveness of the proposed solution.     

Quadtree-based genetic algorithm and its applications to computer vision

Many computer vision problems can be formulated as optimization problems. Presented in this paper is a new framework based on the quadtree-based genetic algorithm that can be applied to solve many of these problems. The proposed algorithm incorporates the quadtree structure into the conventional genetic algorithm. The solutions of image-related problems are encoded through encoding the corresponding quadtrees, and therefore, the 2D locality within a solution can be preserved. Examples addressed using the proposed framework include image segmentation, stereo vision, and motion estimation. In all cases, encouraging results are obtained.     

基于遗传算法的优化排样

介绍了遗传算法以及二维不规则图形的计算机优化排样，研究了二维不规则图形排样中如何建立遗传算法的目标函数，如何实现交叉、变异运算等优化过程，找到了运用遗传算法实现二维不规则图形优化排样的方法。通过计算机编制了运用遗传算法进行二维不规则图形优化排样的运算程序，并对部分样形进行了优化排样，结果比较满意。     

基于DTW算法的语音识别系统实现

动态时间归整(DTW)算法的实现简单有效,在孤立词语音识别系统中得到了广泛的应用.介绍了将DTW算法移植到TMS320VC5402上实现孤立词语音识别的原理、系统硬件组成和软件设计.研究结果表明,系统能满足实时性能要求,识别效果良好.     

基于滑窗结构的软输出维特比算法的实现

文章在对软输出维特比算法(SOVA)进行推导的基础上，分析了软信息的提取过程。同时从硬件实现的角度考虑，提出了一种基于滑动窗1：3结构的SOVA算法实现方案，该算法大大降低了算法实现复杂度和译码延迟，同时通过调整滑窗参数，可以取得与非滑窗SOVA算法几乎相同的性能。     

Revising threshold functions

A revision algorithm is a learning algorithm that identifies the target concept, starting from an initial concept. Such an algorithm is considered efficient if its complexity (in terms of the resource one is interested in) is polynomial in the syntactic distance between the initial and the target concept, but only polylogarithmic in the number of variables in the universe. We give an efficient revision algorithm in the model of learning with equivalence and membership queries for threshold functions, and some negative results showing, for instance, that threshold functions cannot be revised efficiently from either type of query alone. The algorithms work in a general revision model where both deletion and addition type revision operators are allowed.     

Interactive simulated patient—an advanced tool for student-activated learning in medicine and healthcare

An advanced learning system for interactive simulation of patient cases (ISP) is described. The system was designed to meet specific pedagogical needs where student activation and problem orientation were two of the key issues. The system includes features such as video based illness history-taking function using free text input, highly interactive physiological examination procedures, extensive laboratory tests and detailed user feedback. The system was constructed mainly in Authorware Professional, starting more than 10 years ago, and it is now available in refined versions in different languages. Twelve pedagogical and three technical features were initially defined for the project. All of these aims were fulfilled. The ISP system has been successfully used in education, and in international collaboration, including three applicability field tests. The system is also a powerful research tool for studying for instance medical decision-making.     

Context-gated knowledge partitioning in categorization.

According to the knowledge partitioning framework, people sometimes master complex tasks by creating multiple independent parcels of partial knowledge. Research has shown that knowledge parcels may contain mutually contradictory information, and that each parcel may be used without regard to knowledge that is demonstrably present in other parcels. This article reports 4 experiments that investigated knowledge partitioning in categorization. When component boundaries of a complex categorization were identified by a context cue, a significant proportion of participants learned partial and independent categorization strategies that were chosen on the basis of context. For those participants, a strategy used in one context was unaffected by knowledge demonstrably present in other contexts, suggesting that knowledge partitioning in categorization can be complete. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

神经网络系统学习过程初探

本文给出了神经网络系统学习过程的一个随机模型--马尔柯夫模型,从最大熵原理的 观点讨论神经网络系统的学习过程,提出将神经网络系统的学习过程分为两个阶段的想法并 给出学习过程的算法.