32735043
Mining Web Sequential Patterns Using Reinforcement Learning
In this paper, the problem of discovering sequential patterns from the Web log is discussed and an algorithm of sequential patterns mining is brought forward. First, the data in the Web server log file is cleaned and the temporal set about every user is constructed. After the analysis of the temporal set, Markov decision process is applied to model Web log. Then, the sequential patterns of user behaviors are mined by means of reinforcement learning technology. Finally the experiment shows that our mining algorithm is effective.
10779366
An efficient adaptive focused crawler based on ontology learning
The enormous growth of the World Wide Web has made it important to perform resource discovery efficiently. Consequently, several new ideas have been proposed; among them a key technique is focused crawling which is able to crawl particular topical portions of the World Wide Web quickly without having to explore all Web pages. In this paper, we present an intelligent focused crawler algorithm in which we embed ontology to evaluate the page's relevance to the topic. Compared with other algorithms using domain knowledge, our algorithm can evolve the ontology automatically during crawl process. Considering the instinct characteristics of the ontology, propagation has also been imported to accelerate the evolution of the ontology. We applied our approaches in several tasks and provided an empirical evaluation which has shown promising results.
40250467
How to Choose the Optimal Policy in Multi-agent Belief Revision?
In multi-agent system, it is not enough to maintain the coherence of single agent's belief set only. By communication, one agent removing or adding a belief sentence may influence the certainty of other agents' belief sets. In this paper, we investigate carefully some features of belief revision process in different multi-agent systems and bring forward a uniform framework - Bereframe. In Bereframe, we think there are other objects rather than maintaining the coherence or maximize certainty of single belief revision process, and agent must choose the optimal revision policy to realize these. The credibility measure is brought forward, which is used to compute the max credibility degree of the knowledge background. In cooperative multi-agent system, agents always choose the combinative policy to maximize the certainty of whole system's belief set according to the welfare principle. And in semi-competitive multi-agent system, agents choose the revision policy according to the Pareto efficiency principle. In competitive multi-agent system, the Nash equilibrium criterion is applied into agent's belief revision process.
28841129
Believability Based Iterated Belief Revision
Classical iterated belief revision methods rarely take into account the impact of the uncertain information. In this paper, an approach of believability based iterated belief revision(BIBR) is presented. BIBR relates the belief revision in the multi-agent system to the believability of information, which plays an important role in the revision process. Based on the Dempster-Shafer theory of evidence and believability function formalism, the believability of information can be obtained. The revised belief set by BIBR is dependent on the history of revision, namely, on the information received prior to the current belief set. It is proved that the BIBR operation meets the AGM postulates for belief revision and the Darwiche and Pearl postulates for iterated belief revision.
24987149
LMRL: a multi-agent reinforcement learning model and algorithm
Multi-agent reinforcement learning technologies are mainly investigated from two perspectives: one is from the concurrence, and the other from the game theory. The former chiefly applies to cooperative multi-agent systems, while the latter usually applies to coordinated multi-agent systems. However, There exist some problems such as that of the credit assignment and the multiple Nash equilibriums for agents with them. In this paper, we propose a new multi-agent reinforcement learning model and algorithm LMRL from a layer perspective. LMRL model is composed of an offline training layer that employs a single agent reinforcement learning technology to acquire stationary strategy knowledge and an online interaction layer that employs a multi-agent reinforcement learning technology and the strategy knowledge that can be revised dynamically to interact with environment. An agent with LMRL can improve its generalization, adaptability and coordination ability. Experiments show that the performance of LMRL can be better than that of single agent reinforcement learning and Nash-Q.
20003562
Mapping Web Usage Patterns to MDP Model and Mining with Reinforcement Learning
For many web usage mining applications, it is crucial to compare navigation paths of different users. This paper presents a reinforcement learning based method for mining the sequential usage patterns of user behaviors. In detail, the temporal data set about every user is constructed from the web log file, and then the navigation paths of the users are modelled using the extended Markov decision process. The proposed method could learn the dynamical sequential usage patterns on-line.
8115984
Facial expression recognition from line-based caricatures
The automatic recognition of facial expression presents a significant challenge to the pattern analysis and man-machine interaction research community. Recognition from a single static image is particularly a difficult task. In this paper, we present a methodology for facial expression recognition from a single static image using line-based caricatures. The recognition process is completely automatic. It also addresses the computational expensive problem and is thus suitable for real-time applications. The proposed approach uses structural and geometrical features of a user sketched expression model to match the line edge map (LEM) descriptor of an input face image. A disparity measure that is robust to expression variations is defined. The effectiveness of the proposed technique has been evaluated and promising results are obtained. This work has proven the proposed idea that facial expressions can be characterized and recognized by caricatures.
13799231
Improving molecular cancer class discovery through sparse non-negative matrix factorization
MOTIVATION
Identifying different cancer classes or subclasses with similar morphological appearances presents a challenging problem and has important implication in cancer diagnosis and treatment. Clustering based on gene-expression data has been shown to be a powerful method in cancer class discovery. Non-negative matrix factorization is one such method and was shown to be advantageous over other clustering techniques, such as hierarchical clustering or self-organizing maps. In this paper, we investigate the benefit of explicitly enforcing sparseness in the factorization process.


RESULTS
We report an improved unsupervised method for cancer classification by the use of gene-expression profile via sparse non-negative matrix factorization. We demonstrate the improvement by direct comparison with classic non-negative matrix factorization on the three well-studied datasets. In addition, we illustrate how to identify a small subset of co-expressed genes that may be directly involved in cancer.
41738297
Human face profile recognition using attributed string
A new attributed string matching method for human face profile recognition is proposed in this work. It is a novel idea to apply structural and syntactic technique on face profile matching. The approach works on a chain of profile line segments and highlights the favor of curve matching by suppressing the operations of insert and delete. The technique relies mainly on the merge and change operations of string to tackle the inconsistency problem of feature point detection. A quadratic penalty function is proposed to prohibit large angle changes and overmerging. The method produces very encouraging results and is found to be suitable for similar shape classification.
18106801
Mining Protein Sequences for Motifs
We use methods from Data Mining and Knowledge Discovery to design an algorithm for detecting motifs in protein sequences. The algorithm assumes that a motif is constituted by the presence of a "good" combination of residues in appropriate locations of the motif. The algorithm attempts to compile such good combinations into a "pattern dictionary" by processing an aligned training set of protein sequences. The dictionary is subsequently used to detect motifs in new protein sequences. Statistical significance of the detection results are ensured by statistically determining the various parameters of the algorithm. Based on this approach, we have implemented a program called GYM. The Helix-Turn-Helix motif was used as a model system on which to test our program. The program was also extended to detect Homeodomain motifs. The detection results for the two motifs compare favorably with existing programs. In addition, the GYM program provides a lot of useful information about a given protein sequence.
5391334
Sequence homology detection through large scale pattern discovery
Extended Abstract We describe a new approach for idenhifling sequence similatity between a query sequence and a data base of proteins. The central idea is the use of a set ofpattems obtainedfiom the underlying data base through an one-time computation. These patterns are subsequently searchedfor on every query sequence presented to the system. A pattern matched by a region of the quev pinpoints to a potential local similarity between that reg+on and all the data base sequences also matching that pattern. By using a set of prudently chosen patterns, the tool presented in this work is able to discover weak but biologically important similarities.