Microarray datasets are often too large to visualise due to the high dimensionality. the self-organising map has been found useful to analyse massive complex datasets. It can be used for clustering, visualisation, and...
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ISBN:
(纸本)3540228810
Microarray datasets are often too large to visualise due to the high dimensionality. the self-organising map has been found useful to analyse massive complex datasets. It can be used for clustering, visualisation, and dimensionality reduction. However for visualisation purposes the SOM uses colouring schemes as a means of marking cluster boundaries on the map. the distribution of the data and the cluster structures are not faithfully portrayed. In this paper we applied the recently proposed visualisation induced Self-Organising Map (ViSOM), which directly preserves the inter-point distances of the input data on the map as well as the topology. the ViSOM algorithm regularizes the neurons so that the distances between them are proportional in boththe data space and the map space. the results are similar to the Sammon mappings but with improved details on gene distributions and the flexibility to nonlinearity. the method is more suitable for larger datasets.
Heating, Ventilating and Air Conditioning (HVAC) plant is a multi-variable, nonlinear and non minimum phase system, which its control is very difficult. For this reason, in the design of HVAC controller the idea of se...
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ISBN:
(纸本)3540228810
Heating, Ventilating and Air Conditioning (HVAC) plant is a multi-variable, nonlinear and non minimum phase system, which its control is very difficult. For this reason, in the design of HVAC controller the idea of self tuning controllers is being used. In this paper a robust and adaptive self tuning based fuzzy PID controller for control of nonlinear HVAC systems is presented. To illustrate the effectiveness of the proposed method some simulations are given. Obtained results show that the proposed method not only is robust, but also it gives good dynamic response compared with traditional controllers. Also the response time is also very fast despite the fact that the control strategy is based on bounded rationality. To evaluate the usefulness of the proposed method, we compare the response of this method with PID controller. the obtained results show that our method has the better control performance than PID controller.
Web services are the new paradigm for distributed computing. Traditional centralized indexing scheme can't scale well with a large distributed system for a scalable, flexible and robust discovery mechanism. In thi...
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ISBN:
(纸本)3540228810
Web services are the new paradigm for distributed computing. Traditional centralized indexing scheme can't scale well with a large distributed system for a scalable, flexible and robust discovery mechanism. In this paper, we use an ontology-based approach to capture real world knowledge for the finest granularity annotation of Web services. this is the core for intelligent discovery. We use a distributed hash table (DHT) based catalog service in P2P (peer to peer) system to index the ontology information and store the index at peers. We have discussed the DHT based service discovery model and discovery procedure. DHT supports only exact match lookups. We have made improvement to the matching algorithm for intelligent services discovery. the experiments show that the discovery model has good scalability and the semantic annotation can notably improve discovery exactness. the improved algorithms can discover the most potential service against request.
We present an approach to classification of biomedical terms based on the information acquired automatically from the corpus of relevant literature. the learning phase consists of two stages: acquisition of terminolog...
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ISBN:
(纸本)3540228810
We present an approach to classification of biomedical terms based on the information acquired automatically from the corpus of relevant literature. the learning phase consists of two stages: acquisition of terminologically relevant contextual patterns (CPs) and selection of classes that apply to terms used withthese patterns. CPs represent a generalisation of similar term contexts in the form of regular expressions containing lexical, syntactic and terminological information. the most probable classes for the training terms co-occurring withthe statistically relevant CP are learned by a genetic algorithm. Term classification is based on the learnt results. First, each term is associated withthe most frequently co-occurring CP. Classes attached to such CP are initially suggested as the term's potential classes. then, the term is finally mapped to the most similar suggested class.
We study classification when the majority of data is unlabeled, and only a small fraction is labeled: the so-called semi-supervised learning situation. Blum and Mitchell's co-training is a popular semi-supervised ...
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ISBN:
(纸本)3540228810
We study classification when the majority of data is unlabeled, and only a small fraction is labeled: the so-called semi-supervised learning situation. Blum and Mitchell's co-training is a popular semi-supervised algorithm [1] to use when we have multiple independent views of the entities to classify. An example of a multi-view situation is classifying web pages: one view may describe the pages by the words that occur on them, another view describes the pages by the words in the hyperlinks that point to them. In co-training two learners each form a model from the labeled data and then incrementally label small subsets of the unlabeled data for each other. the learners then re-estimate their model from the labeled data and the psuedo-labels provided by the learners. though some analysis of the algorithm's performance exists [1] the computation performed is still not well understood. We propose that each view in co-training is effectively performing incremental EM as postulated by Neal and Hinton [3], combined with a Bayesian classifier. this analysis suggests improvements over the core co-training algorithm. We introduce variations, which result in faster convergence to the maximum possible accuracy of classification than the core co-training algorithm, and therefore increase the learning efficiency. We empirically verify our claim for a number of data sets in the context of belief network learning.
this paper presents a tool for web usage mining. the aim is centered on providing a tool that facilitates the mining process rather than implement elaborated algorithms and techniques. the tool covers different phases...
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ISBN:
(纸本)9783540772255
this paper presents a tool for web usage mining. the aim is centered on providing a tool that facilitates the mining process rather than implement elaborated algorithms and techniques. the tool covers different phases of the CRISP-DM methodology as data, preparation, data selection, modeling and evaluation. the algorithms used in the modeling phase are those implemented in the Weka project. the tool has been tested in a web site to find access and navigation patterns.
In help of BYY harmony learning for binary independent factor analysis, an automatic oriental medical diagnostic approach is proposed. A preliminary experiment has shown a promising result on the diagnostic problem of...
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Artificial intelligence is an emerging area in the field of industrial engineering. the paper identifies the key application areas of AI in industrial engineering like intelligent Retrieval from database, Expert Syste...
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ISBN:
(纸本)1860584403
Artificial intelligence is an emerging area in the field of industrial engineering. the paper identifies the key application areas of AI in industrial engineering like intelligent Retrieval from database, Expert Systems, automated Programming, Scheduling Problems, Perception Problems, Pattern Matching and Recognition, Robotics, Computer Vision, Heuristic Classification, Machine learning, Neural Networks, etc.
Non-negative Matrix Factorization (NMF), especially with sparseness constraints, plays a critically important role in dataengineering and machine learning. Hoyer (2004) presented an algorithm to compute NMF with exac...
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ISBN:
(纸本)9783319462578;9783319462561
Non-negative Matrix Factorization (NMF), especially with sparseness constraints, plays a critically important role in dataengineering and machine learning. Hoyer (2004) presented an algorithm to compute NMF with exact sparseness constraints. the exact sparseness constraints depends on a projection operator. In the present work, we first give a very simple counterexample, for which the projection operator of the Hoyer (2004) algorithm fails. After analysing the reason geometrically, we fix this bug by adding some random terms and show that the fixed one works correctly. Based on the fixed projection operator, we propose another sparse NMF algorithm aiming at optimizing the generalized Kullback-Leibler divergence, hence named SNMF-GKLD. Experimental results show that SNMF-GKLD not only has similar effects with Hoyer (2004) on the same data sets, but is also efficient.
this work investigates learning and generalisation capabilities of Radial Basis Function Networks used to solve function regression and classification tasks in the environmental context. In particular RBFN is applied ...
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ISBN:
(纸本)9783642153808
this work investigates learning and generalisation capabilities of Radial Basis Function Networks used to solve function regression and classification tasks in the environmental context. In particular RBFN is applied to solve the problem of snow cover thickness estimation in which critical aspects such as minimal training condition, weak pattern description and inconsistency among data arise. the RBFN shows good performances and high flexibility in coping with regression, hard and soft classifications which are complementary tasks in the analysis of complex environmental phenomena.
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