the proceedings contain 15 papers. the special focus in this conference is on Agents and Data Mining Interaction. the topics include: patternrecognition in Online Environment by Data Mining Approach;a Multiple System...
ISBN:
(纸本)9783642154195
the proceedings contain 15 papers. the special focus in this conference is on Agents and Data Mining Interaction. the topics include: patternrecognition in Online Environment by Data Mining Approach;a Multiple System Performance Monitoring Model for Web Services;implementing an Open Reference Architecture based on Web Service Mining for the Integration of Distributed Applications and Multi-Agent Systems;minority Game Data Mining for Stock Market Predictions;integrating Workflow into Agent-based Distributed Data Mining Systems;pilot Study: Agent-based Exploration of Complex Data in a Hospital Environment;multi-agent Information Retrieval in Heterogeneous Industrial Automation Environments;a Data Mining Approach to Identify Obligation Norms in Agent Societies;probabilistic Modeling of Mobile Agents’ Trajectories;real-Time Sensory pattern Mining for Autonomous Agents;analyzing Agent-based Simulations of Inter-organizational Networks;clustering in a Multi-Agent Data Mining Environment.
Ontology mapping tools typically employ combinations of methods, the mutual effects of which deserve study. We propose an approach to analysis of such combinations using synthetic ontologies. Initial experiments have ...
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Ontology mapping tools typically employ combinations of methods, the mutual effects of which deserve study. We propose an approach to analysis of such combinations using synthetic ontologies. Initial experiments have been carried out for two string-based and one graph-based method. Most important target of the study was the impact of name patterns over taxonomy paths on the mapping results.
the proceedings contain 14 papers. the topics discussed include: finding useful items and links in social and agent networks;integrating workflow into agent-based distributed data mining systems;pilot study: agent-bas...
ISBN:
(纸本)3642154190
the proceedings contain 14 papers. the topics discussed include: finding useful items and links in social and agent networks;integrating workflow into agent-based distributed data mining systems;pilot study: agent-based exploration of complex data in a hospital environment;multi-agent information retrieval in heterogeneous industrial automation environments;a data mining approach to identify obligation norms in agent societies;probabilistic modeling of mobile agents' trajectories;real-time sensory pattern mining for autonomous agents;analyzing agent-based simulations of inter-organizational networks;clustering in a multi-agent data mining environment;patternrecognition in online environment by data mining approach;implementing an open reference architecture based on web service mining for the integration of distributed applications and multi-agent systems;and minority game data mining for stock market predictions.
Interpolating classifiers interpolate all the training data and thus have zero training error. Recent research shows their fundamental importance for high-performance ensemble techniques. Interpolation kernel machines...
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Although inexact graph-matching is a problem of potentially exponential complexity, the problem may be simplified by decomposing the graphs to be matched into smaller subgraphs. If this is done, then the process may c...
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Learning contextual text embeddings that represent causal graphs has been useful in improving the performance of downstream tasks like causal treatment effect estimation. However, existing causal embeddings which are ...
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ISBN:
(纸本)9781954085527
Learning contextual text embeddings that represent causal graphs has been useful in improving the performance of downstream tasks like causal treatment effect estimation. However, existing causal embeddings which are trained to predict direct causal links, fail to capture other indirect causal links of the graph, thus leading to spurious correlations in downstream tasks. In this paper, we define the faithfulness property of contextual embeddings to capture geometric distance-based properties of directed acyclic causal graphs. By incorporating these faithfulness properties, we learn text embeddings that are 31.3% more faithful to human validated causal graphs with about 800K and 200K causal links and achieve 21.1% better Precision-Recall AUC in a link prediction fine-tuning task. Further, in a crowdsourced causal question-answering task on Yahoo! Answers with questions of the form "What causes X?", our faithful embeddings achieved a precision of the first ranked answer (P@1) of 41.07%, outperforming the existing baseline by 10.2%.
In this paper we propose biometric descriptors inspired by shape signatures traditionally used in graphics recognition approaches. In particular several methods based on line shape descriptors used to identify newborn...
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ISBN:
(纸本)3540347119
In this paper we propose biometric descriptors inspired by shape signatures traditionally used in graphics recognition approaches. In particular several methods based on line shape descriptors used to identify newborns from the biometric information of the ears are developed. the process steps are the following: image acquisition, ear segmentation, ear normalization, feature extraction and identification. Several shape signatures are defined from contour images. these are formulated in terms of zoning and contour crossings descriptors. Experimental results are presented to demonstrate the effectiveness of the used techniques.
Knowledge graph embedding aims at learning low-dimensional representations for entities and relations in knowledge graph. Previous knowledge graph embedding methods usually assign a score to each triple in order to me...
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ISBN:
(数字)9789811534126
ISBN:
(纸本)9789811534126;9789811534119
Knowledge graph embedding aims at learning low-dimensional representations for entities and relations in knowledge graph. Previous knowledge graph embedding methods usually assign a score to each triple in order to measure the plausibility of it. Despite of the effectiveness of these models, they ignore the fine-grained (matching signals between entities and relations) clues since their scores are mainly obtained by manipulating the triple as a whole. To address this problem, we instead propose a model which firstly produces diverse features of entity and relation by multi-head attention and then introduces the interaction mechanism to incorporate matching signals between entities and relations. Experiments show that our model achieves better link prediction performance than multiple strong baselines on two benchmark datasets WN18RR and FB15k-237.
In this paper, the cellular neural network (CNN) with ratio memory (RM) is implemented in CMOS to recognize and classify the image patterns. In the implemented CMOS CNN, the BJT-based combined four-quadrant multiplier...
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In this paper, the cellular neural network (CNN) with ratio memory (RM) is implemented in CMOS to recognize and classify the image patterns. In the implemented CMOS CNN, the BJT-based combined four-quadrant multiplier and two-quadrant divider with separated magnitude and sign is used to implement the Hebbien learning function and the ratio memory. thus, the combined multiplier and divider and the CNN have simple structure and large input/output signal range. the pattern learning and recognition function of the 9×9 CNN with RM is simulated by both Matlab software and HSPICE. It has been verified that the CNN with RM has the advantages of more stored patterns for processing, and longer memory time with feature enhancement as compared to the CNN without RM. thus the proposed CNN with RM has great potential in the applications of neural associate memory for image processing.
this article explores the feasibility of a market-ready, mobile patternrecognition system based on the latest findings in the field of object recognition and currently available hardware and network technology. More ...
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ISBN:
(纸本)9783642147579
this article explores the feasibility of a market-ready, mobile patternrecognition system based on the latest findings in the field of object recognition and currently available hardware and network technology. More precisely, an innovative, mobile museum guide system is presented, which enables camera phones to recognize paintings in art galleries. After careful examination, the algorithms Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) were found most promising for tins goal. Consequently, both have been integrated in a fully implemented prototype system and their performance has been thoroughly evaluated under realistic conditions. In order to speed up the matching process for finding the corresponding sample in the feature database, an approximation to Nearest Neighbor Search was investigated. the k-means based clustering approach was found to significantly improve the computational time.
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