Kernel methods are effective machinelearning techniques for many image based patternrecognition problems. Incorporating 3D information is useful in such applications. The optical profilometries and interforometric t...
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ISBN:
(纸本)9780819485830
Kernel methods are effective machinelearning techniques for many image based patternrecognition problems. Incorporating 3D information is useful in such applications. The optical profilometries and interforometric techniques provide 3D information in an implicit form. Typically phase unwrapping process, which is often hindered by the presence of noises, spots of low intensity modulation, and instability of the solutions, is applied to retrieve the proper depth information. In certain applications such as patternrecognition problems, the goal is to classify the 3D objects in the image, rather than to simply display or reconstruct them. In this paper we present a technique for constructing kernels on the measured data directly without explicit phase unwrapping. Such a kernel will naturally incorporate the 3D depth information and can be used to improve the systems involving 3D object analysis and classification.
Ensemble methods are used in many patternrecognition problems to improve the classification accuracy. Thus, in this paper, the key goal is to evaluate the performance of three popular ensemble methods bagging, boosti...
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Web caching is a technology for improving network traffic on the internet. It is a temporary storage of Web objects (such as HTML documents) for later retrieval. There are three significant advantages to Web caching;r...
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ISBN:
(纸本)9781612842127
Web caching is a technology for improving network traffic on the internet. It is a temporary storage of Web objects (such as HTML documents) for later retrieval. There are three significant advantages to Web caching;reduced bandwidth consumption, reduced server load, and reduced latency. These rewards have made the Web less expensive with better performance. The aim of this research is to introduce advanced machinelearning approaches for Web caching to decide either to cache or not to the cache server, which could be modelled as a classification problem. The challenges include identifying attributes ranking and significant improvements in the classification accuracy. Four methods are employed in this research;Classification and Regression Trees (CART), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF) and TreeNet (TN) are used for classification on Web caching. The experimental results reveal that CART performed extremely well in classifying Web objects from the existing log data and an excellent attribute to consider for an accomplishment of Web cache performance enhancement.
datamining concerns theories,methodologies,and in particular,computer systems for knowledge extraction or mining from large amounts of *** rule mining is a general purpose rule discovery *** has been widely used for ...
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ISBN:
(纸本)9781612848396
datamining concerns theories,methodologies,and in particular,computer systems for knowledge extraction or mining from large amounts of *** rule mining is a general purpose rule discovery *** has been widely used for discovering rules in medical *** diagnosis of diseases is a significant and tedious task in *** detection of heart disease from various factors or symptoms is an issue which is not free from false presumptions often accompanied by unpredictable *** the effort to utilize knowledge and experience of numerous specialists and clinical screening data of patients collected in databases to facilitate the diagnosis process is considered a valuable *** this paper,we presented an efficient approach for the prediction of heart attack risk levels from the heart disease ***, the heart disease database is clustered using the K-means clustering algorithm,which will extract the data relevant to heart attack from the *** approach allows mastering the number of fragments through its k parameter. Subsequently the frequent patterns are mined from the extracted data,relevant to heart disease,using the MAFIA (Maximal Frequent Itemset Algorithm)*** machinelearning algorithm is trained with the selected significant patterns for the effective prediction of heart attack. We have employed the ID3 algorithm as the training algorithm to show level of heart attack with the decision *** results showed that the designed prediction system is capable of predicting the heart attack effectively.
Developing an effective medical diagnosis system for many diseases, such as thyroid gland disease, to assist physicians in hospitals has become a high priority for many researchers and clinical centers. In fact, exist...
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The proceedings contain 63 papers. The topics discussed include: text mining: finding right documents from large collection of unstructured documents;exploiting the solution structure knowledge to speedup non-learning...
ISBN:
(纸本)9788988678480
The proceedings contain 63 papers. The topics discussed include: text mining: finding right documents from large collection of unstructured documents;exploiting the solution structure knowledge to speedup non-learning planner;outlier degree estimation in various sensor data for building maintenance using K-means clustering and Markov model;a hybrid multi class classifier based on artificial immune algorithm and support vector machine;optimized very fast decision tree with balanced classification accuracy and compact tree size;OVFDT with functional tree leaf-majority class, naïve Bayes and adaptive hybrid integrations;a similar harmonic method for broadcasting non-linear videos;keyword elicitation for patent retrieval by using bibliographic information;firm financial distress factor analysis;forecasting analysis for global copper clad laminate market;and comparison plan for data warehouse system architectures.
Kernel Principal Component Analysis (KPCA) is a widely used technique in the dimension reduction, de-noising and discovering nonlinear intrinsic dimensions of data set. In this paper we describe a reweighing kernel-ba...
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ISBN:
(纸本)9781612848372
Kernel Principal Component Analysis (KPCA) is a widely used technique in the dimension reduction, de-noising and discovering nonlinear intrinsic dimensions of data set. In this paper we describe a reweighing kernel-based classification method for improving recognition problem. Firstly, we map the training samples to the feature space by non-linear transformation, and then perform principal component analysis(PCA) using the selected kernel function in the feature space, and get the linear representation of testing samples in the feature space. Secondly, by using the idea of reweighting, we select the similarity between testing sample and each training sample as the weight of reweighting, then take the final weight as the criteria of classification. The experimental results demonstrate that our method is more accurate than Support Vector machine (SVM) classification method and Linear Discriminant Analysis (LDA) classification. In addition, the number of training samples that our method need is much smaller than some other methods.
The proceedings contain 45 papers. The topics discussed include: high order fuzzy time series for exchange rates forecasting;an O(N) clustering method on ultrametric data;time series similarity search based on middle ...
ISBN:
(纸本)9781612842127
The proceedings contain 45 papers. The topics discussed include: high order fuzzy time series for exchange rates forecasting;an O(N) clustering method on ultrametric data;time series similarity search based on middle points and clipping;datamining technique for expertise search in a special interest group knowledge portal;harmony search algorithm for flexible manufacturing system (FMS) machine loading problem;talent knowledge acquisition using datamining classification techniques;public domain datasets for optimizing network intrusion and machinelearning approaches;frequent pattern using multiple attribute value for itemset generation;harmony search algorithm for optimal word size in symbolic time series representation;neural network based soft sensor for prediction of biopolycaprolactone molecular weight using bootstrap neural network technique;and an efficient mining of transactional data using graph-based technique.
In the SFB/TR29 a focus lies on human factors and their integration into Industrial Product-Service Systems (IPS2). These innovative systems are complex and dynamic. Human operators need to be able to perform a multit...
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ISBN:
(纸本)9783642217982;9783642217999
In the SFB/TR29 a focus lies on human factors and their integration into Industrial Product-Service Systems (IPS2). These innovative systems are complex and dynamic. Human operators need to be able to perform a multitude of complex tasks in such socio-technical systems, providing a challenge to the operators because of the high complexity. Therefore automatic assistance systems are necessary for the overall reliability and effectiveness of such a system. This article describes a theoretical approach for simulating human behavior with cognitive models. The performed actions are recognized with motion capturing in combination with machinelearning. By evaluating the perceived action and reality a description for the situation can be automatically generated in real time. This can be used for e. g. providing the human operator with real time contextual feedback.
The proceedings contain 7 papers. The topics discussed include: learning-based entity resolution with MapReduce;incremental recomputations in MapReduce;efficient data distribution strategy for join query processing in...
ISBN:
(纸本)9781450309561
The proceedings contain 7 papers. The topics discussed include: learning-based entity resolution with MapReduce;incremental recomputations in MapReduce;efficient data distribution strategy for join query processing in the cloud;the panel of experts cloud pattern;authentication of range query results in MapReduce environments;trustworthy middleware services in the Cloud;and Teledata: datamining, social network analysis and statistics analysis system based on Cloud computing in telecommunication industry.
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