444260
A new strategy for selecting working sets applied in SMO
At present sequential minimal optimization (SMO) is one of the most popular and efficient training algorithms for support vector machines (SVM), especially for large-scale problems. A novel strategy for selecting working sets applied in SMO is presented in the paper. Based on the original feasible direction method, the new strategy also takes the efficiency of kernel cache maintained in SMO into consideration. It is shown in the experiments on the well-known data sets that computation of the kernel function and training time is reduced greatly, especially for the problems with many samples and support vectors.
5468007
A robust motion planner for assembly robots
An emergency-tolerant topological motion planning method for manipulators in an assembly workspace is presented. The topological motion planning method is discussed. The method first partitions the configuration space into several connected blocks and constructs a characteristic network (CN). It then searches for a path in CN. The partition of C-space in a topological method is accurate and complete, which is especially suitable for guiding an emergency-tolerant robot.<<ETX>>
14861274
Learning in Region-Based Image Retrieval
In this paper, several effective learning algorithms using global image representations are adjusted and introduced to regionbased image retrieval (RBIR). First, the query point movement technique is considered. By assembling all the segmented regions of positive examples together and resizing the regions to emphasize the latest positive examples, a composite image is formed as the new query. Second, the application of support vector machines (SVM) in relevance feedback for RBIR is investigated. Both the one class SVM as a class distribution estimator and two classes SVM as a classifier are taken into account. For the latter, two representative display strategies are studied. Last, a region re-weighting algorithm is proposed inspired by those feature re-weighting ones. Experimental results on a database of 10,000 general-purpose images demonstrate the effectiveness of the proposed learning algorithms.
44569612
A New Cache Replacement Algorithm in SMO
In the methods for training Support Vector Machines (SVM), precomputed elements in the Hessian matrix are usually cached in order to avoid recomputation. However, the least-recent-used replacement algorithm that is widely used is not suitable since the elements are requested in an irregular way. A new cache replacement algorithm applied in Sequential Minimal Optimization (SMO) is presented in the paper. The item corresponding to the component with minimal violation of the Karush-Kuhn-Tucher (KKT) condition is deleted to make room for new one when the cache is full. It is shown in the experiments that the hit ratio of the cache is improved compared with LRU cache while the training time can be reduced in the tasks where the computation of elements in Hessian matrix is very time-consuming.
2491447
Unsupervised image segmentation using local homogeneity analysis
In this paper, a novel method is presented for unsupervised image segmentation based on local homogeneity analysis. First, a criterion for homogeneity of a certain pattern is proposed. Applying the criterion to local windows in the original image results in the "H-image". The high and low values of the H-image correspond to possible region boundaries and region interiors respectively. Then, a region growing method is used to segment the image based on the H-image. Finally, visually similar regions are merged together to avoid over-segmentation. Experimental results on real images show the effectiveness and robustness of the method.
4535096
Coupled kernel-based subspace learning
It was prescriptive that an image matrix was transformed into a vector before the kernel-based subspace learning. In this paper, we take the kernel discriminant analysis (KDA) algorithm as an example to perform kernel analysis on 2D image matrices directly. First, each image matrix is decomposed as the product of two orthogonal matrices and a diagonal one by using singular value decomposition; then an image matrix is expanded to be of higher or even infinite dimensions by applying the kernel trick on the column vectors of the two orthogonal matrices; finally, two coupled discriminative kernel subspaces are iteratively learned for dimensionality reduction by optimizing the Fisher criterion measured by Frobenius norm. The derived algorithm, called coupled kernel discriminant analysis (CKDA), effectively utilizes the underlying spatial structure of objects and the discriminating information is encoded in two coupled kernel subspaces respectively. The experiments on real face databases compared with KDA and Fisherface validate the effectiveness of CKDA.
1232795
Efficient text classification by weighted proximal SVM
In this paper, we present an algorithm that can classify large-scale text data with high classification quality and fast training speed. Our method is based on a novel extension of the proximal SVM mode (Fung and Mangasarian, 2001). Previous studies on proximal SVM have focused on classification for low dimensional data and did not consider the unbalanced data cases. Such methods will meet difficulties when classifying unbalanced and high dimensional data sets such as text documents. In this work, we extend the original proximal SVM by learning a weight for each training error. We show that the classification algorithm based on this model is capable of handling high dimensional and unbalanced data. In the experiments, we compare our method with the original proximal SVM (as a special case of our algorithm) and the standard SVM (such as SVM light) on the recently published RCV1-v2 dataset. The results show that our proposed method had comparable classification quality with the standard SVM. At the same time, both the time and memory consumption of our method are less than that of the standard SVM.
27379775
Convergence of a New Decomposition Algorithm for Support Vector Machines
Decomposition methods is the main way for solving support vector machines (SVMs) with large data sets. In this paper a new decomposition algorithm is proposed, and its convergence is also proved.
2174685
Privacy-enhancing personalized web search
Personalized web search is a promising way to improve search quality by customizing search results for people with individual information goals. However, users are uncomfortable with exposing private preference information to search engines. On the other hand, privacy is not absolute, and often can be compromised if there is a gain in service or profitability to the user. Thus, a balance must be struck between search quality and privacy protection. This paper presents a scalable way for users to automatically build rich user profiles. These profiles summarize a user.s interests into a hierarchical organization according to specific interests. Two parameters for specifying privacy requirements are proposed to help the user to choose the content and degree of detail of the profile information that is exposed to the search engine. Experiments showed that the user profile improved search quality when compared to standard MSN rankings. More importantly, results verified our hypothesis that a significant improvement on search quality can be achieved by only sharing some higher-level user profile information, which is potentially less sensitive than detailed personal information.
36606756
Optimization of combustion control based on fuzzy logic
In this paper, to reach energy-saving goal, fuzzy system is employed to get optimal setpoints of furnace temperature. In the proposed approach, the optimal temperature trajectory of a slab in the furnace is determined through fuzzy system and the setpoint of furnace temperature is calculated according to this optimal trajectory. Because the approach calculates the setpoints of furnace temperature at each computation period, it can deal with disturbance of system to a certain extent. The simulation results show validity of the proposed method.
37845872
The complexity of learning in PLN networks
Abstract In this paper, the complexity of learning in the feedforward PLN network is investigated by using Markov chain theory, when its training samples are incomplete (i.e., a network with hidden nodes). We present a learning algorithm. A formula for computing the average number of steps that the learning algorithm converges is obtained when the PLN network exists a solution. In the probabilistic sense, the completeness of the learning algorithm is proved. Some computer simulations are given to verify the analysis.