Nesting Algorithm for Multi-Classification Problems
Support vector machines (SVMs) are originally designed for binary classifications. As for multi-classifications, they are usually converted into binary ones. In the conventional multi-classifiable algorithms, One-against-One algorithm is a very power method. However, there exists a middle unclassifiable region. In order to overcome this drawback, a novel method called Nesting Algorithm is presented in this paper. Our ideas are as follows: firstly, construct the optimal hyperplanes based on One-against-One approach. Secondly, if there exist data points in the middle unclassifiable region, select them to construct the optimal hyperplanes with the same hyperparameters. Thirdly, repeat the second step until there are no data points in the unclassifiable region or the region is disappeared. In this paper, we also prove the validity of the proposed algorithm for unclassifiable region and give the computational complexity analysis of the method. In order to examine the training accuracy and the generalization performance of the proposed algorithm, One-against-One algorithm, fuzzy least square support vector machine (FLS-SVM) and the proposed algorithm are applied to five UCI datasets. The results show that the training accuracy of the proposed algorithm is higher than the others, and its generalization performance is also comparable with them.

title: Nesting One-Against-One Algorithm Based on SVMs for Pattern Classification
abstract: Support vector machines (SVMs), which were originally designed for binary classifications, are an excellent tool for machine learning. For the multiclass classifications, they are usually converted into binary ones before they can be used to classify the examples. In the one-against-one algorithm with SVMs, there exists an unclassifiable region where the data samples cannot be classified by its decision function. This paper extends the one-against-one algorithm to handle this problem. We also give the convergence and computational complexity analysis of the proposed method. Finally, one-against-one, fuzzy decision function (FDF), and decision-directed acyclic graph (DDAG) algorithms and our proposed method are compared using five University of California at Irvine (UCI) data sets. The results report that the proposed method can handle the unclassifiable region better than others.

title: A Learning Process Using SVMs for Multi-agents Decision Classification
abstract: In order to resolve decision classification problem in multiple agents system, this paper first introduces the architecture of multiple agents system. It then proposes a support vector machines based assessment approach, which has the ability to learn the rules form previous assessment results from domain experts. Finally, the experiment are conducted on the artificially dataset to illustrate how the proposed works, and the results show the proposed method has effective learning ability for decision classification problems.

title: Twi-Map Support Vector Machine for Multi-classification Problems
abstract: In this paper, a novel method called Twi-Map Support Vector Machines (TMSVM) for multi-classification problems is presented. Our ideas are as follows: Firstly, the training data set is mapped into a high-dimensional feature space. Secondly, we calculate the distances between the training data points and hyperplanes. Thirdly, we view the new vector consisting of the distances as new training data point. Finally, we map the new training data points into another high-dimensional feature space with the same kernel function and construct the optimal hyperplanes. In order to examine the training accuracy and the generalization performance of the proposed algorithm, One-against-One algorithm, Fuzzy Least Square Support Vector Machine (FLS-SVM) and the proposed algorithm are applied to five UCI data sets. Comparison results obtained by using three algorithms are given. The results show that the training accuracy and the testing one of the proposed algorithm are higher than those of One-against-One and FLS-SVM.

title: Multi-Space-Mapped SVMs for Multi-class Classification
abstract: In SVMs-based multiple classification, it is not always possible to find an appropriate kernel function to map all the classes from different distribution functions into a feature space where they are linearly separable from each other. This is even worse if the number of classes is very large. As a result, the classification accuracy is not as good as expected. In order to improve the performance of SVMs-based multi-classifiers, this paper proposes a method, named multi-space-mapped SVMs, to map the classes into different feature spaces and then classify them. The proposed method reduces the requirements for the kernel function. Substantial experiments have been conducted on one-against-all, one-against-one, FSVM, DDAG algorithms and our algorithm using six UCI data sets. The statistical results show that the proposed method has a higher probability of finding appropriate kernel functions than traditional methods and outperforms others.

title: Binary Tree Support Vector Machine Based on Kernel Fisher Discriminant for Multi-classification
abstract: In order to improve the accuracy of the conventional algorithms for multi-classifications, we propose a binary tree support vector machine based on Kernel Fisher Discriminant in this paper. To examine the training accuracy and the generalization performance of the proposed algorithm, One-against-All, One-against-One and the proposed algorithms are applied to five UCI data sets. The experimental results show that in general, the training and the testing accuracy of the proposed algorithm is the best one, and there exist no unclassifiable regions in the proposed algorithm.

title: Complex Analysis of Anisotropic Swarms with Gaussian Profiles
abstract: This paper considers an M-member "individual-based" continuous time swarm model with individuals moving with a nutrient profile (or an attractant/repellent) in an n- dimensional space. It is proved that the swarm members aggregate and eventually form a cohesive cluster of finite size for Gaussian Profiles. Moreover, all the swarm members will converge to more favorable areas of the Gaussian profile under certain conditions.

title: A Novel Fuzzy Neural Network with Fast Training and Accurate Generalization
abstract: For the reason of all parameters in a conventional fuzzy neural network (FNN) needed to be adjusted iteratively, learning can be very slow and may suffer from local minima. To overcome these problems, we propose a novel FNN in this paper, which shows a fast speed and accurate generalization. First we state the universal approximation theorem for an FNN with random membership function parameters (FNN-RM). Since all the membership function parameters are arbitrarily chosen, the proposed FNN-RM algorithm needs to adjust only the output weights of FNNs. Experimental results on function approximation and classification problems show that the new algorithm not only provides thousands of times of speed-up over traditional learning algorithms, but also produces better generalization performance in comparison to other FNNs.

title: The Design of the GPS-Based Surveying Robot Automatic Monitoring System for Underground Mining Safety
abstract: Earth subsidence in underground mining is an unavoidable problem in mining production, and timely and scientific observation and early warning is one of the important factors in the security of mining production. Though the surveying robot (i.e. automatic electronic total station) can automatically (or semi-automatically) monitor ground deformation for underground mining, the stability of the station location (monitor base station) has great impact on the monitor precision and when the measurement vision is covered, the surveying robot fails to monitor the corresponding deformation point. In order to tackle the above problem, the author and the research team have integrated the technology of GPS (Global Positioning System) with surveying robot and developed the GPS-based surveying robot automatic monitoring system for underground mining safety, which completely solves the foresaid problem, simplifies the monitor program and reduces the fixed investment cost of monitor. The article introduces the structure and working principle of the GPS-based surveying robot automatic monitoring system for underground mining safety, presents examples of monitor.

title: Region-of-interest coding of 3D mesh based on wavelet transform
abstract: A scheme for the region of interest (ROI) coding of 3D meshes is proposed for the first time. The ROI is encoded with higher fidelity than the rest region, and the "priority" of ROI relative to the rest region (background, BG) can be specified by encoder or decoder (user). Wavelet transform is used on 3D mesh and zerotrees are adopted to organize the coefficients. The wavelet coefficients of ROI are scaled up and encoded with a modified set partitioning in hierarchical trees (SPIHT) algorithm. In additional, a fast algorithm is proposed for creating the ROI mask. Once the quality of reconstructed ROI becomes high enough, the transmission can be intermitted and much transmission bandwidth and storage space will be saved consequently.

title: Classification using support vector machines with graded resolution
abstract: A method which we call support vector machine with graded resolution (SVM-GR) is proposed in this paper. During the training of the SVM-GR, we first form data granules to train the SVM-GR and remove those data granules that are not support vectors. We then use the remaining training samples to train the SVM-GR. Compared with the traditional SVM, our SVM-GR algorithm requires fewer training samples and support vectors, hence the computational time and memory requirements for the SVM-GR are much smaller than those of a conventional SVM that use the entire dataset. Experiments on benchmark data sets show that the generalization performance of the SVM-GR is comparable to the traditional SVM.