13758087
Support cluster machine
For large-scale classification problems, the training samples can be clustered beforehand as a downsampling pre-process, and then only the obtained clusters are used for training. Motivated by such assumption, we proposed a classification algorithm, Support Cluster Machine (SCM), within the learning framework introduced by Vapnik. For the SCM, a compatible kernel is adopted such that a similarity measure can be handled not only between clusters in the training phase but also between a cluster and a vector in the testing phase. We also proved that the SCM is a general extension of the SVM with the RBF kernel. The experimental results confirm that the SCM is very effective for largescale classification problems due to significantly reduced computational costs for both training and testing and comparable classification accuracies. As a by-product, it provides a promising approach to dealing with privacy-preserving data mining problems.
672476
Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction
The sparsity problem in collaborative filtering (CF) is a major bottleneck for most CF methods. In this paper, we consider a novel approach for alleviating the sparsity problem in CF by transferring useritem rating patterns from a dense auxiliary rating matrix in other domains (e.g., a popular movie rating website) to a sparse rating matrix in a target domain (e.g., a new book rating website). We do not require that the users and items in the two domains be identical or even overlap. Based on the limited ratings in the target matrix, we establish a bridge between the two rating matrices at a cluster-level of user-item rating patterns in order to transfer more useful knowledge from the auxiliary task domain. We first compress the ratings in the auxiliary rating matrix into an informative and yet compact cluster-level rating pattern representation referred to as a codebook. Then, we propose an efficient algorithm for reconstructing the target rating matrix by expanding the codebook. We perform extensive empirical tests to show that our method is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary tasks, as compared to many state-of-the-art CF methods.
17395966
Scene segmentation based on video structure and spectral methods
Scene is an important semantic unit for video analysis, retrieval and browsing. However, due to the lack of a generic algorithm, many studies focus on specific methods for certain video genes, e.g., news, sports, etc. In this paper, we propose a general framework for scene segmentation. First, we construct a graph, in which the elements encode the shot-to-shot coherent characteristics of a video clip based on visual similarity and temporal relation between shots. In this step, we only exploit the inherent property of video itself and it is independent of video genres. Second, spectral method is applied on the graph to group shots into scenes. The proposed method is not only simple but also effective to deal with organized features. Experimental results validate the robustness of our method on different kinds of videos.
1252893
A Configurable Web Service Performance Testing Framework
More and more softwares based on web service technologies are developed. Before their releases on the Internet, it is necessary to evaluate these systems' performance, especially their response time under different workload pressures. However, existing performance testing benchmarks and tools for web service applications are difficult to adapt to various user-specific testing purposes. This paper proposes a configurable web service performance testing framework which contains client module, application server module and database module. Client module, by using the network cooperation method that one central client drives several other clients, adapts to a great number of concurrent customers to request web services. Application server module contains web services under testing and external supporting web services, each of which is configured as a plug-in. The process to realize mixed ratio of web service interactions is similar to dealing cards and adapts to different commercial application characteristics. In database module, the data model including table and attribute dependence can be customized, and the data scale initialization can be resized according to the topology of above dependence. As such, this framework allows testers to dynamically define their data model, customize their scale of database, configure their transaction characteristics, deploy their application strategies and confirm their performance metrics..
8899940
Sketch retrieval based on spatial relations
In this paper, we propose an effective approach for sketch retrieval. Given sketches are firstly decomposed into basic geometric primitives and represented as a topological graph that encodes both the intrinsic attributes of the primitives and their relationships. The graph spectrum descriptor is then adopted to translate the graph-match into the computation of vector distances. Sketches retrieval is finally done by means of similarity calculation of feature vectors based on dimensionality reduction. The relevance feedback is also introduced to refine the retrieval results. Experiments prove the effectiveness and efficiency of the proposed method in sketch content retrieval and user independent.
26704614
A New Approach to Forecast Crude Oil Price Based on Fuzzy Neural Network
A new oil price forecasting model based on fuzzy neural network, which combings RBF neural network, Markov chain based semi parametric model and wavelet analysis is proposed in this paper. The high degree of prediction accuracy confirms the performance of the model in accurate forecasts, reinforcement learning properties and mapping capabilities.
7181717
A Biased Support Vector Machine Approach to Web Filtering
Web filtering is an inductive process which automatically builds a filter by learning the description of user interest from a set of pre-assigned web pages, and uses the filter to assign unprocessed web pages. In web filtering, content similarity analysis is the core problem, the automatic-learning and relativity-analysis abilities of machine learning algorithms help solve the above problems and make ML useful in web filtering. While in practical applications, different filtering task implies different userinterest and thus implies different filtering result. This work studies how to adjust the web filtering results to be more fit for the user interest. The web filtering result are divided into three categories: relative pages, similar pages and homologous pages according to different user interest. A Biased Support Vector Machine (BSVM) algorithm, which imports a stimulant function, uses training examples distribution n+/n−− and a user-adaptable parameter k to deal imbalancedly different classes of the pre-assigned pages, is introduced to adjust the filtering result to be best fit for the user interest. Experiments show that BSVM can greatly improve the web filtering performance.
3472016
Detecting doubly compressed JPEG images by using Mode Based First Digit Features
In this paper, we utilize the probabilities of the first digits of quantized DCT (discrete cosine transform) coefficients from individual AC (alternate current) modes to detect doubly compressed JPEG images. Our proposed features, named by mode based first digit features (MBFDF), have been shown to outperform all previous methods on discriminating doubly compressed JPEG images from singly compressed JPEG images. Furthermore, combining the MBFDF with a multi-class classification strategy can be exploited to identify the quality factor in the primary JPEG compression, thus successfully revealing the double JPEG compression history of a given JPEG image.