the proceedings contain 68 papers. the topics discussed include: a comparative analysis of data distribution methods in an agent-based neural system for classification tasks;stochastic differential portfolio games wit...
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ISBN:
(纸本)0769526624
the proceedings contain 68 papers. the topics discussed include: a comparative analysis of data distribution methods in an agent-based neural system for classification tasks;stochastic differential portfolio games with regime switching model;extracting symbolic rules from clustering of gene expression data;a novel microarray gene selection method based on consistency;combining greedy method and genetic algorithm to identify transcription factor binding sites;investigation of a new artificial immune system model applied to patternrecognition;RLM: a new method of encoding weights in DNA strands;shape representation and distance measure based on retational graph;fast modeling of curved object from two images;research on an improved gray gradient orientation algorithm in anisotropic high-pass filtering;and image color reduction based on self-organizing maps and growing self-organizing neural networks.
this paper explores the customized learning from specific to general for classification learning. Our novel learning framework called SUPE customizes its learning process to the instance to be classified called query ...
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ISBN:
(纸本)3540335846
this paper explores the customized learning from specific to general for classification learning. Our novel learning framework called SUPE customizes its learning process to the instance to be classified called query instance. the data representation in SUPE is also customized to the query instance. Given a query instance, the training data is transformed into a query matrix, from which useful patterns are discovered for learning. the final prediction of the class label is performed by combining some statistics of the discovered useful patterns. We show that SUPE conducts the search from specific to general in a significantly reduced hypothesis space. the query matrix also facilitates the complicated operations in SUPE. the experimental results on benchmark data sets are encouraging.
Glaucoma is a common disease of the eye that often results in partial blindness. the main symptom of glaucoma is the progressive deterioration of the visual field. Glaucoma management involves monitoring the progress ...
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ISBN:
(纸本)3540335846
Glaucoma is a common disease of the eye that often results in partial blindness. the main symptom of glaucoma is the progressive deterioration of the visual field. Glaucoma management involves monitoring the progress of the disease using regular visual field tests but currently there is no standard method for classifying changes in visual field measurements. Sequence matching techniques typically rely on similarity measures. However, visual field measurements are very noisy, particularly in people with glaucoma. It is therefore difficult to establish a reference data set including both stable and progressive visual fields. We describe method that uses a baseline computed from a query sequence, to match stable sequences in a database collected from volunteers. the results suggest that the new method is more accurate than other techniques for identifying progressive sequences, though there is a small penalty for stable sequences.
the proceedings contain 42 papers. the topics discussed include: can biological motion be a biometric?;indexing of document images based on nine-directional codes;steganalysis of LSB embedded images using variable thr...
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ISBN:
(纸本)1424406110
the proceedings contain 42 papers. the topics discussed include: can biological motion be a biometric?;indexing of document images based on nine-directional codes;steganalysis of LSB embedded images using variable threshold color pair analysis;mosaic generation in H.264 compressed domain;an energy-aware adaptive clustering protocol for sensor networks;distributed asynchronous clustering for self-organisation of wireless sensor networks;routing protocols for landslide prediction using wireless sensor networks;analysis of DNA spectrograms using machinelearning methods;Fisher linear discriminant analysis based technique useful for efficient character recognition;data sharing strategy for guaranteeing quality-of-service in VoD application;linear feature extraction using combined approach of Hough transform, eigen values and raster scan algorithms;and a heuristic detection network: an adaptive DDoS control.
the proceedings contain 115 papers. the topics discussed include: strategy coordination approach for safe learning about novel filtering strategies in multi agent framework;modeling and design of agent based open deci...
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ISBN:
(纸本)3540335846
the proceedings contain 115 papers. the topics discussed include: strategy coordination approach for safe learning about novel filtering strategies in multi agent framework;modeling and design of agent based open decision support systems;particle filter method for a centralized multisensor system;design and analysis of a novel load-balancing model based on mobile agent;construction and simulation of the movable propeller turbine neural network model;research and application of data mining in power plant process control and optimization;an efficient algorithm for incremental mining of sequential patterns;repeating pattern discovery from audio stream;a method to eliminate incompatible knowledge and equivalence knowledge;a similarity-aware multiagent-based web content management;evolutionary multi-objective optimization algorithm with preference for mechanical design;and refinement of fuzzy production rules by using a fuzzy-neural approach.
Mining of sequential patterns is an important issue among the various data mining problems. the problem of incremental mining of sequential patterns deserves as much attention. In this paper, we consider the problem o...
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ISBN:
(纸本)3540335846
Mining of sequential patterns is an important issue among the various data mining problems. the problem of incremental mining of sequential patterns deserves as much attention. In this paper, we consider the problem of the incremental updating of sequential pattern mining when some transactions and/or data sequences are deleted from the original sequence database. We present a new algorithm, called IU_D, for mining frequent sequences so as to make full use of information obtained during an earlier mining process for reducing the cost of finding new sequential patterns in the updated database. the results of our experiment show that the algorithm performs significantly faster than the naive approach of mining the entire updated database from scratch.
Traditional RAID has the characteristics that location of stripe unit in each disk is stochastic and static, and that the outer zone of the disk has higher data transfer rate as compared to the inner one. Facing this ...
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ISBN:
(纸本)3540335846
Traditional RAID has the characteristics that location of stripe unit in each disk is stochastic and static, and that the outer zone of the disk has higher data transfer rate as compared to the inner one. Facing this situation, to exploit RAID I/O performance fully, this paper proposes a new algorithm PMSH (Placement and Migration based on Stripe unit Heat) for RAID stripe unit data to be placed optically and migrated dynamically. Based on the heat of RAID stripe unit, PMSH keeps migrating the frequently accessed stripe unit to the disk zone with higher data transfer rate to optimize the location of data in RAID disks and make the data distribution adapt to the evolution of file access pattern dynamically as well. Simulation results demonstrate significant RAID I/O performance improvement using PMSH.
In supervised machinelearning, the partitioning of the values (also called grouping) of a categorical attribute aims at constructing a new synthetic attribute which keeps the information of the initial attribute and ...
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ISBN:
(纸本)3540269231
In supervised machinelearning, the partitioning of the values (also called grouping) of a categorical attribute aims at constructing a new synthetic attribute which keeps the information of the initial attribute and reduces the number of its values. In case of very large number of values, the risk of overfilling the data increases sharply and building good groupings becomes difficult. In this paper, we propose two new grouping methods founded on a Bayesian approach, leading to Bayes optimal groupings. the first method exploits a standard schema for grouping models and the second one extends this schema by managing a "garbage" group dedicated to the least frequent values. Extensive comparative experiments demonstrate that the new grouping methods build high quality groupings in terms of predictive quality, robustness and small number of groups.
Concept lattice, core structure in Formal Concept Analysis has been used in various fields like software engineering and knowledge discovery. In this paper, we present the integration of Association rules and Classifi...
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ISBN:
(纸本)3540269231
Concept lattice, core structure in Formal Concept Analysis has been used in various fields like software engineering and knowledge discovery. In this paper, we present the integration of Association rules and Classification rules using Concept Lattice. this gives more accurate classifiers for Classification. the algorithm used is incremental in nature. Any increase in the number of classes, attributes or transactions does not require the access to the previous database. the incremental behavior is very useful in finding classification rules for real time data such as image processing. the algorithm requires just one database pass through the entire database. Individual classes can have different support threshold and pruning conditions such as criteria for noise and number of conditions in the classifier.
Spatial data mining is a demanding field since huge amounts of spatial data have been collected in various applications, ranging form Remote Sensing to GIS, Computer Cartography, Environmental Assessment and Planning....
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ISBN:
(纸本)3540269231
Spatial data mining is a demanding field since huge amounts of spatial data have been collected in various applications, ranging form Remote Sensing to GIS, Computer Cartography, Environmental Assessment and Planning. Although there have been efforts for spatial association rule mining, but mostly researchers discuss only the positive spatial association rules;they have not considered the spatial negative association rules. Negative association rules are very useful in some spatial problems and are capable of extracting some useful and previously unknown hidden information. We have proposed a novel approach of mining spatial positive and negative association rules. the approach applies multiple level spatial mining methods to extract interesting patterns in spatial and/or non-spatial predicates. data and spatial predicates/association-ship are organized as set hierarchies to mine them level-by-level as required for multilevel spatial positive and negative association rules. A pruning strategy is used in our approach to efficiently reduce the search space. Further efficiency is gained by interestingness measure.
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