27213248
Multiresolution recognition of offline handwritten Chinese characters with wavelet transform
The authors propose a novel multiresolution recognition scheme for handwritten Chinese character recognition in which an input pattern is recognized by adopting the coefficients of the wavelet transforms. It is known that wavelet representation provides a coarse-to-fine strategy. The recognition starts from the coarse scale and moves to the finer scales. After preprocessing, the wavelet transform is applied to the kanji image. Then, we make use of the coefficients with the lowest resolution to select 50 candidates from 3755 categories. In order to enhance the statistical feature of a character, we used statistical methods to reconstruct features in fine classification. With the proposed recognition system, experiments are performed on the 863 Testing System. The correct rate reaches 80.56%, which is a promising result.
30724966
Motivational System for Human-Robot Interaction
Human-Robot Interaction (HRI) has recently drawn increased attention. Robots can not only passively receive information but also actively emit actions. We present a motivational system for human-robot interaction. The motivational system signals the occurrence of salient sensory inputs, modulates the mapping from sensory inputs to action outputs, and evaluates candidate actions. No salient feature is predefined in the motivational system but instead novelty based on experience, which is applicable to any task. Novelty is defined as an innate drive. Reinforcer is integrated with novelty. Thus, the motivational system of a robot can be developed through interactions with trainers. We treat vision-based neck action selection as a behavior guided by the motivational system. The experimental results are consistent with the attention mechanism in human infants.
39883279
Identifying Clusters in the Web Graph by Using Local Search
43590220
Lower Bounds and Parameterized Approach for Longest Common Subsequence
In this paper, different parameterized versions of the longest common subsequence (LCS) problem are extensively investigated and computational lower bound results are derived based on current research progress in parameterized computation. For example, with the number of sequences as the parameter k, the problem is unlikely to be solvable in time f(k)no(k), where n is the length of each sequence and f is any recursive function. The lower bound result is asymptotically tight in consideration of the dynamic programming approach of time O(nk). Computational lower bounds for polynomial-time approximation schemes (PTAS) for the LCS problem are also derived. It is shown that the LCS problem has no PTAS of time f(1/ e)no(1/ e) for any recursive function f, unless all SNP problems are solvable in subexponential time. Compared with former results on this problem, this result has its significance. Finally a parameterized approach for the LCS problem is discussed, which is more efficient than the dynamic programming approach, especially when applied to large scale sequences.
410602
Using self-supervised word segmentation in Chinese information retrieval
We propose a self-supervised word-segmentation technique for Chinese information retrieval. This method combines the advantages of traditional dictionary based approaches with character based approaches, while overcoming many of their shortcomings. Experiments on TREC data show comparable performance to both the dictionary based and the character based approaches. However, our method is language independent and unsupervised, which provides a promising avenue for constructing accurate multilingual information retrieval systems that are flexible and adaptive.
16726691
Objective and subjective algorithms for grouping association rules
We propose two algorithms for grouping and summarizing association rules. The first algorithm recursively groups rules according to the structure of the rules and generates a tree of clusters as a result. The second algorithm groups the rules according to the semantic distance between the rules by making use of an automatically tagged semantic tree-structured network of items. We provide a case study in which the proposed algorithms are evaluated. The results show that our grouping methods are effective and produce good grouping results.
945376
Text classification in Asian languages without word segmentation
We present a simple approach for Asian language text classification without word segmentation, based on statistical -gram language modeling. In particular, we examine Chinese and Japanese text classification. With character -gram models, our approach avoids word segmentation. However, unlike traditional ad hoc -gram models, the statistical language modeling based approach has strong information theoretic basis and avoids explicit feature selection procedure which potentially loses significantly amount of useful information. We systematically study the key factors in language modeling and their influence on classification. Experiments on Chinese TREC and Japanese NTCIR topic detection show that the simple approach can achieve better performance compared to traditional approaches while avoiding word segmentation, which demonstrates its superiority in Asian language text classification.