A Simplified Learning Algorithm of Incremental Bayesian
A proportion factor is constructed though the Maximum Aposteriori Probability of examples in test data to select the training examples in incremental learning process. Instead of complex normal classify loss expression, the proportion factor λ is used to estimate the classify loss to improve classification efficiency. The final experiment shows that this algorithm is feasible, and more accurate than simple Bayesian classifier. The computing time is highly reduced on the optimal selection of examples in incremental learning process.

title: An Improved Immune Evolutionary Algorithm for Multimodal Function Optimization
abstract: Based on the inspiration of immune system, a new multi-objective optimization algorithm is presented. The proposed approach adopts a cluster mechanism in order to divide the population into subpopulations for the stage of selection and reproduction. In the immune clonal selection process, a hybrid hypermutation operator is introduced to improve the variety of antibodies and affinity maturation, thus it can quickly obtain the global and local optima. The simulation results illustrated that the efficiency of the proposed algorithm for complicated function optimization and verified it's remarkable quality of the global and local convergence reliability.

title: The Predictive Control of Sintering Temperature in Rotary Kiln Based on Image Feedback and Soft Computing
abstract: The sintering temperature in rotary kiln is measured through image processing algorithm as feedback of its predictive control. The predictive model based on radial basis function (RBF) network is designed by the image and other process data optimized by genetic algorithm. The final results of application at an alumina sintering kiln show that this intelligent predictive control strategy can achieve higher stability in normal condition.

title: Interweave Neural Networks with Evolutionary Algorithms, Cellular Computing, Bayesian Learning and Ensemble Learning
abstract: A neural networks are able to give solutions to complex problems in business intelligence and financial engineering due to their nonlinear processing. This paper consists of a survey of various business intelligence and financial engineering and so on applications based on the neural networks, and also a summary of the recent techniques such as still evolutionary algorithms, cellular computing, Bayesian learning and ensemble learning. The neural networks can be powerful tools in several fields of business intelligence and financial engineering. It can be concluded that, the future of neural networks lies in their use in conjunction with other advanced technologies.

title: Image Mining: Issues, Frameworks and Techniques
abstract: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper.

title: Systematic benchmarking of microarray data feature extraction and classification
abstract: A combination of microarrays with classification methods is a promising approach to supporting clinical management decisions in oncology. The aim of this paper is to systematically benchmark the role of classification models. Each classification model is a combination of one feature extraction method and one classification method. We consider four feature extraction methods and five classification methods, from which 20 classification models can be derived. The feature extraction methods are t-statistics, non-parametric Wilcoxon statistics, ad hoc signal-to-noise statistics, and principal component analysis (PCA), and the classification methods are Fisher linear discriminant analysis (FLDA), the support vector machine (SVM), the k nearest-neighbour classifier (kNN), diagonal linear discriminant analysis (DLDA), and diagonal quadratic discriminant analysis (DQDA). Twenty randomizations of each of three binary cancer classification problems derived from publicly available datasets are examined. PCA plus FLDA is found to be the optimal classification model.

title: A New Heuristic Reduct Algorithm Base on Rough Sets Theory
abstract: Real world data sets usually have many features, which increases the complexity of data mining task. Feature selection, as a preprocessing step to the data mining, has been shown very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving comprehensibility. To find the optimal feature subsets is the aim of feature selection. Rough sets theory provides a mathematical approach to find optimal feature subset, but this approach is time consuming. In this paper, we propose a novel heuristic algorithm based on rough sets theory to find out the feature subset. This algorithm employs appearing frequency of attribute as heuristic information. Experiment results show in most times our algorithm can find out optimal feature subset quickly and efficiently.

title: Effect of energy feedbacks on Virtual Slope Walking: I. Complementary Energy Feedback
abstract: This paper presents our study over the effect of Complementary Energy Feedback on Virtual Slope Walking, while Virtual Slope Walking is our new biped gait generation method inspired by Passive Dynamic Walking. The energy feedback strength is defined and the walking is modeled as a step-to-step function. The Jacobi Matrix eigenvalues of the function are calculated together with the basin of attraction. From the analysis, we find the characteristic of Complementary Energy Feedback is being effective on a fast gait but weak on a slow one. By making use of the Complementary Energy Feedback in walking experiment, our robot achieves speed change from 1.5leg/s to 4.1leg/s.