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.
patternrecognition of hand gesture is currently research hot spot. It is important for rehabilitation training, human-computer interaction, prosthetic control and sports science research etc. The brachioradialis, ext...
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ISBN:
(纸本)9783642238956
patternrecognition of hand gesture is currently research hot spot. It is important for rehabilitation training, human-computer interaction, prosthetic control and sports science research etc. The brachioradialis, extensor digitorum communis, flexor carpi ulnaris muscle and flexor carpi radialis muscle as signal acquisition points;this paper captures four channel sEMG signals. Aiming at the sEMG signals of hand gesture, this paper uses the eigenvalue processed by RMS and MOV as training data samples, which is regarded as the input of LVQ neural network. Through training and learning samples, the better training result is got. The results of the study indicate that the LVQ neural network can effectively identify three action modes, all fingers, relax and middle, by adopting the four channel sEMG signals. The simple algorithm, small calculation and more than 89 percent recognition rate shows that it is a very good method of patternrecognition.
Discriminant analysis is an important multivariate statistical analysis, and plays an important part in pattern classification, datamining, machinelearning et al. In this paper, based on principle of progressively s...
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ISBN:
(纸本)9783037850992
Discriminant analysis is an important multivariate statistical analysis, and plays an important part in pattern classification, datamining, machinelearning et al. In this paper, based on principle of progressively statistical discriminant analysis under Fisher rule, a progressively statistical discriminant model is set up. The authors analyzed the data about the occurrence of the second generation of the corn borer in 21 years from 1985 to 2006 (except 1990) at Linyi, Shandong Province, and then set up three graded recognitionpattern. The results tested the pest data showed that the fitting rate is 95.24%, 92.31% and 100% respectively, and that accuracy of forecast is satisfactory.
The two mature disciplines, namely datamining and data Warehousing have broadly the same set of objectives. Yet, they have developed largely separate from each other resulting in different techniques being used in ea...
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ISBN:
(纸本)9783642221842
The two mature disciplines, namely datamining and data Warehousing have broadly the same set of objectives. Yet, they have developed largely separate from each other resulting in different techniques being used in each discipline. It has been recognized that mining techniques developed for patternrecognition such as Clustering and Visualization can assist in designing data warehouse schema. However, a suitable methodology is required for the seamless integration of mining methods in the design of warehouse schema. In previous work, we presented a methodology that employs hierarchical clustering to derive a tree structure that can be used by a data warehouse designer to build a schema. We believe that, in order to strengthen the decision making process, there is a strong need for a method that automatically extracts knowledge present at different levels of abstraction from a warehouse. We demonstrate with examples how mining at different levels of a hierarchical warehouse schema can give new insights about the underlying data cluster which not only helps in building more meaningful dimensions and facts for data warehouse design but can also improve the decision making process.
Most feed-forward artificial neural network training algorithms for classification problems are based on an iterative steepest descent technique. Their well-known drawback is slow convergence. A fast solution is an Ex...
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