20577453
A Novel Clustering Method Based on SVM
For the problem of cluster analysis, the objective function based algorithms are popular and widely used methods. However, the performance of these algorithms depends upon the priori information about cluster number and cluster prototypes. Moreover, it is only effective for analyzing data set with the same type of cluster prototypes. For this end, this paper presents a novel algorithm based on support vector machine (SVM) for realizing fully unsupervised clustering. The experimental results with various test data sets illustrate the effectiveness of the proposed novel clustering algorithm based on SVM.
8123307
Strategies anticipating a difference in search depth using opponent-model search
In this contribution we propose a class of strategies which focus on the game as well as on the opponent. Preference is given to the thoughts of the opponent, so that the strategy under investigation might be speculative. We describe a generalization of OM search, called (D, d)-OM search, where D stands for the depth of search by the player and d for the opponent's depth of search. A known difference in search depth can be exploited by purposely choosing a suboptimal variation with the aim to gain a larger advantage than when playing the objectively best move. The difference in search depth may have the result that the opponent does not see the variation in sufficiently deep detail. We then give a pruning alternative for (D, d)-OM search, denoted by α-β 2 pruning. A best-case analysis shows that α-β 2 prunes very efficiently, comparable to the efficiency of α-β with regard to minimax. The effectiveness of the proposed strategy is confirmed by simulations using a game-tree model including an opponent model and by experiments in the domain of Othello.
7186941
A Cartoon Video Detection Method Based on Active Relevance Feedback and SVM
By analyzing the particular features of visual content for cartoon videos, 8 typical features of MPEG-7 descriptors are extracted to distinguish the cartoons from other videos. Then, a content-based video classifier is developed by combining the active relevance feedback technique and SVM for detecting the cartoon videos. The experimental results on the vast real video clips illustrate that compared with the classifier based on SVM and that based on traditional relevance feedback technique and SVM, the proposed classifier has a higher advantage of cartoon video detection.
28211985
A Fully Unsupervised Image Segmentation Algorithm Based on Wavelet-Domain Hidden Markov Tree Models
A fully unsupervised image segmentation algorithm is presented in this paper, in which wavelet-domain hidden Markov tree (WD-HMT) model is exploited together with the cluster analysis and validity techniques. The true number of textures in a given image is determined by calculating the likelihood disparity of textures using the modified partition fuzzy degree (MPFD) function at one suitable scale. Then, possibilistic C-means (PCM) clustering is performed to determine the training sample data from different textures according to the true number of textures obtained. The unsupervised segmentation is changed into self-supervised one, and the HMTseg algorithm is used to achieve the final segmentation results. This algorithm is applied to segment a variety of composite texture images into distinct homogeneous regions and good segmentation results are reported.
35059796
A Unified Framework for Shot Boundary Detection
According to drawbacks of available algorithms,a new hierarchical and multiresolution approach to the detection and classification of scene breaks in video sequences is presented in this paper. This method gives a unified framework for different shots by using the multi-resolution analysis as mainstay. Firstly, the video clips are cut by FCM clustering method, then the fade and dissolve are detected using ECR and SCD algorithm in the high components and the low components respectively by integer-to-integer wavelet transform. Finally, according to the cut and the former detection of gradual changes, we use motion vectors in high-components of 3D-WT to detect the wipe transition. Experimental results with real video clips about 7h demonstrate that our method can detect and classify a variety of scene breaks, including cuts, fades and dissolves, even in sequences involving significant motions and flash.
2376280
A Radar Target Multi-feature Fusion Classifier Based on Rough Neural Network
Fusing Multi-feature will benefit radar target classification with much more belief. However, since radar target attributes such as high range resolution profiles, waveforms, frequency spectra, time-frequency spectra, higher order statistics, polarization spectra and flight path are of different dimensions, it is hard to make decision by fusing multi-feature directly. Fortunately, rough set makes decision by examining the fitness of each condition attribute separately, while neural network is powerful for dealing with nonlinear problems. With radial projection of target dimension, cruising velocity and height as condition attribute, a multi-feature rough neural network fusion classifier is presented. Simulation of the proposed classifier based on an information system with 25 targets belonging to 6 classes shows accuracy not less than 93%, while attributes are subjoined with typical radar errors.
19030773
Standardized evaluation method for Web clustering results
Web clustering assists users of a search engine by presenting search results as clusters of related pages. Many clustering algorithms with different characteristics have been developed: but the lack of a standardized Web clustering evaluation method that can evaluate clusterings with different characteristics has prevented effective comparison of algorithms. The paper solves this by introducing a new structure for defining general ideal clusterings and new measurements for evaluating clusterings with different characteristics by comparing them against the general ideal clustering.
40240913
Combining Contents and Citations for Scientific Document Classification
This paper introduces a classification system that exploits the content information as well as citation structure for scientific paper classification. The system first applies a content-based statistical classification method which is similar to general text classification. We investigate several classification methods including K-nearest neighbours, nearest centroid, naive Bayes and decision trees. Among those methods, the K-nearest neighbours is found to outperform others while the rest perform comparably. Using phrases in addition to words and a good feature selection strategy such as information gain can improve system accuracy and reduce training time in comparison with using words only. To combine citation links for classification, the system proposes an iterative method to update the labellings of classified instances using citation links. Our results show that, combining contents and citations significantly improves the system performance.
40362881
A Knowledge Learning Approach to Information Extraction from Multiple Text Based Web Sites
This paper describes a learning/adaptive approach to automatically building knowledge bases for information extraction from text based web pages. A frame based representation is introduced to represent domain knowledge as knowledge unit frames. A frame learning algorithm is developed to automatically learn knowledge unit frames from training examples. Some training examples can be obtained by automatically parsing a number of tabular web pages in the same domain, which greatly reduced the amount of time consuming manual work. This approach was investigated on ten web sites of real estate advertisements and car advertisements and nearly all the information was successfully extracted with very few false alarms. These results suggest that both the knowledge unit frame representation and the frame learning algorithm work well, domain specific knowledge bases can be learned from training examples, and the domain specific knowledge base can be used for information extraction from flexible text-based semi-structured Web pages on multiple Web sites. The investigation of the knowledge representation on five other domains suggests that this approach can be easily applied to other domains by simply changing the training examples.
2838896
SplitTester : software to identify domains responsible for functional divergence in protein family
BackgroundMany protein families have undergone functional divergence after gene duplications such that current subgroups of the family carry out overlapping but distinct biological roles. For the protein families with known functional subtypes (a functional split), we developed the software, SplitTester, to identify potential regions that are responsible for the observed distinct functional subtypes within the same protein family.ResultsOur software, SplitTester, takes a multiple protein sequences alignment as input, generated from protein members of two subgroups with known functional divergence. SplitTester was designed to construct the neighbor joining tree (a split cluster) from variable-sized sliding windows across the alignment in a process called split-clustering. SplitTester identifies the regions, whose split cluster is consistent with the functional split, but may be inconsistent with the phylogeny of the protein family. We hypothesize that at least some number of these identified regions, which are not following a random mutation process, are responsible for the observed functional split. To test our method, we used reverse transcriptase from a group of Pseudoviridae retrotransposons: to identify residues specific for diverged primer recognition. Candidate regions were then mapped onto the three dimensional structures of reverse transcriptase. The locations of these amino acids within the enzyme are consistent with their biological roles.ConclusionSplitTester aims to identify specific domain sequences responsible for functional divergence of subgroups within a protein family. From the analysis of retroelements reverse transcriptase family, we successfully identified the regions splitting this family according to the primer specificity, implying their functions in the specific primer selection.
1168192
Approximately Repetitive Structure Detection for Wrapper Induction
In recent years, much work has been invested into automatically learning wrappers for information extraction from HTML tables and lists. Our research has focused on a system that can learn a wrapper from a single unlabelled page. An essential step is to locate the tabular data within the page. This is not trivial when the structures of data tuples are similar but not identical. In this paper we describe an algorithm that can automatically detect approximate repetitive structures within one sequence. The algorithm does not rely on any domain knowledge or HTML heuristics and it can be used in detecting repetitive patterns and hence to learn wrappers from a single unlabeled tabular page.