986422
Individuality of numerals
The analysis of handwritten documents from the view-pointof determining their writership has great bearing onthe criminal justice system. In many cases, only a limitedamount of handwriting is available and sometimes it consistsof only numerals. Using a large number of handwrittennumeral images extracted from about 3000 samples writtenby 1000 writers, a study of the individuality of numerals foridentification/verification purposes was conducted. The individualityof numerals was studied using cluster analysis.Numerals discriminability was measured for writer verification.The study shows that some numerals present a higherdiscriminatory power and that their performances for theverification/identification tasks are very different.
58797822
Discriminatory power of handwritten words for writer recognition
Analysis of allographs (characters) and allograph combinations (words) is the key for the identification/verification of a writer's handwriting. While allographs are usually part of words and the segmentation of a word into allographs is a subjective process, analysis of handwritten words is a natural option, complementary to allograph and document-level analysis. We consider four different types of features obtained using both segmentation-based and segmentation-free approaches: (i) GSC (gradient, structural and concavity) features that are extracted from the cells of a grid superimposed on the word image (ii) WMR (word model recognizer) features, extracted from the cells of superimposed grids on the segmented characters (iii) SC (shape curvature) features that describe characters by the distribution of curvature values on their contours and (iv) SCON (shape context) features that measure the similarity between character contour shapes. Their individual and accumulated performance is evaluated for the writer identification and verification tasks on over 75000 words images, written by more than 1000 writers. Experimental results show that handwritten words are very effective in discriminating handwriting and that both segmentation-free and segmentation-based approaches are valid.
15108545
Individuality of handwritten characters
Analysis of handwritten characters (allographs) plays animportant role in forensic document examination. However,so far there lacks a comprehensive and quantitative study onindividuality of handwritten characters. Based on a largenumber of handwritten characters extracted from handwritingsamples of 1000 individuals in US, the individuality ofhandwritten characters has been quantitatively measuredthrough identification and verification models. Our studyshows that in general alphabetic characters bear more individualitythan numerals and use of a certain number ofcharacters will significantly outperform the global featuresof handwriting samples in handwriting identification andverification. Moreover, the quantitative measurement ofdiscriminative powers of characters offers a general guidancefor selecting most-informative characters in examiningforensic documents.
18211870
Construction of handwriting databases using transcript-based mapping
A recognition-based system was developed for constructing handwriting databases. The system automatically recognizes the word and the character images in handwritten document images by applying a transcript mapping algorithm. The transcript-mapping process is modeled as an optimization problem involving multiple word-segmentation hypotheses, word recognition and word alignment. The extensive experiments show that a large number of character and word images can be automatically extracted with very high reliability.
6883417
Fast k-nearest neighbor classification using cluster-based trees
Most fast k-nearest neighbor (k-NN) algorithms exploit metric properties of distance measures for reducing computation cost and a few can work effectively on both metric and nonmetric measures. We propose a cluster-based tree algorithm to accelerate k-NN classification without any presuppositions about the metric form and properties of a dissimilarity measure. A mechanism of early decision making and minimal side-operations for choosing searching paths largely contribute to the efficiency of the algorithm. The algorithm is evaluated through extensive experiments over standard NIST and MNIST databases.
2642244
CrossChip: a system supporting comparative analysis of different generations of Affymetrix arrays
SUMMARY
To increase compatibility between different generations of Affymetrix GeneChip arrays, we propose a method of filtering probes based on their sequences. Our method is implemented as a web-based service for downloading necessary materials for converting the raw data files (*.CEL) for comparative analysis. The user can specify the appropriate level of filtering by setting the criteria for the minimum overlap length between probe sequences and the minimum number of usable probe pairs per probe set. Our website supports a within-species comparison for human and mouse GeneChip arrays.


AVAILABILITY
http://www.crosschip.org
9400771
Identification of biochemical networks by S-tree based genetic programming
MOTIVATION
Most previous approaches to model biochemical networks have focused either on the characterization of a network structure with a number of components or on the estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions among the components and the dynamic behaviors of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to exist in the real system.


RESULTS
We propose a new representation named S-trees for both the structural and dynamical modeling of a biochemical network within a unified scheme. We further present S-tree based genetic programming to identify the structure of a biochemical network and to estimate the corresponding parameter values at the same time. While other evolutionary algorithms require additional techniques for sparse structure identification, our approach can automatically assemble the sparse primitives of a biochemical network in an efficient way. We evaluate our algorithm on the dynamic profiles of an artificial genetic network. In 20 trials for four settings, we obtain the true structure and their relative squared errors are <5% regardless of releasing constraints about structural sparseness. In addition, we confirm that the proposed algorithm is robust within +/-10% noise ratio. Furthermore, the proposed approach ensures a reasonable estimate of a real yeast fermentation pathway. The comparatively less important connections with non-zero parameters can be detected even though their orders are below 10(-2). To demonstrate the usefulness of the proposed algorithm for real experimental biological data, we provide an additional example on the transcriptional network of SOS response to DNA damage in Escherichia coli. We confirm that the proposed algorithm can successfully identify the true structure except only one relation.
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.
22470543
A Weighted Technique in Heuristic Search
As shown in [1], we examine search as a statistic sampling process. Based on some statistical inference method the probability that a subtree in search tree contains the goal can be decided. Thus some weight is in tentionaly added to the evaluation function of those nodes which are unlikely in the solution path so that the search will concentrate on the most promising path. It results in a new weighted algorithm-WSA. 
 
Tn a uniform m-ary tree, we show that a goal can be found by WSA in the polynomial time, although the computational complexity of A (or A*) may be O(e) for searching the same space. Where N is the depth at which the goal is located.
62394
Support vector machine learning for image retrieval
A novel method of relevance feedback is presented based on support vector machine learning in the content-based image retrieval system. A SVM classifier can be learned from training data of relevance images and irrelevance images marked by users. Using the classifier, the system can retrieve more images relevant to the query in the database efficiently. Experiments were carried out on a large-size database of 9918 images. It shows that the interactive learning and retrieval process can find correct images increasingly. It also shows the generalization ability of SVM under the condition of limited training samples.