Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic patternrecognition and datamining techniques to detect the ons...
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ISBN:
(数字)9781510604315
ISBN:
(纸本)9781510604315
Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic patternrecognition and datamining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing patternrecognition techniques used in EEG analysis leverage the notion of supervised machinelearning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.
In tackling datamining and patternrecognition tasks, finding a compact but effective set of features is often a crucial step in the whole problem solving process. In this paper we present an empirical study on featu...
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ISBN:
(纸本)9780769527307
In tackling datamining and patternrecognition tasks, finding a compact but effective set of features is often a crucial step in the whole problem solving process. In this paper we present an empirical study on feature selection for classical instrument recognition, using machinelearning techniques to select and evaluate features extracted from a number of different feature schemes in terms of their classification performance. It is revealed that there is significant redundancy in existing feature schemes commonly used in practice. Our results suggest that further feature analysis research is necessary for optimising feature selection for the instrument recognition problem.
In recent years, high-throughput genome sequencing and sequence analysis technologies have created the need for automated annotation and analysis of large sets of genes. The Gene Ontology (GO) provides a common contro...
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ISBN:
(纸本)9783540689706
In recent years, high-throughput genome sequencing and sequence analysis technologies have created the need for automated annotation and analysis of large sets of genes. The Gene Ontology (GO) provides a common controlled vocabulary for describing gene function however the process for annotating proteins with GO terms is usually through a tedious manual curation process by trained professional annotators. With the wealth of genomic data that are now available, there is a need for accurate automated annotation methods. In this paper, we propose a method for automatically predicting GO terms for proteins by applying statistical patternrecognition techniques. We employ protein functional domains as features and learn independent Support Vector machine classifiers for each GO term. This approach creates sparse data sets with highly imbalanced class distribution. We show that these problems can be overcome with standard feature and instance selection methods. We also present a meta-learning scheme that utilizes multiple SVMs trained for each GO term, resulting in improved overall performance than either SVM can achieve alone. The implementation of the tool is available at http://***/AAPFC.
A fast support vector machine (SVM) training algorithm is proposed under SVM's decomposition framework by effectively integrating kernel caching, digest and shrinking policies and stopping conditions. Kernel cachi...
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A fast support vector machine (SVM) training algorithm is proposed under SVM's decomposition framework by effectively integrating kernel caching, digest and shrinking policies and stopping conditions. Kernel caching plays a key role in reducing the number of kernel evaluations by maximal reusage of cached kernel elements. Extensive experiments have been conducted on a large handwritten digit database MNIst to show that the proposed algorithm is much faster than Keerthi et al.'s improved SMO, about nine times. Combined with principal component analysis, the total training for ten one-against-the-rest classifiers on MNIst took less than an hour. Moreover, the proposed fast algorithm speeds up SVM training without sacrificing the generalization performance. The 0.6% error rate on MNIst test set has been achieved. The promising scalability of the proposed scheme paves a new way to solve more large-scale learning problems in other domains such as datamining.
A fast support vector machine (SVM) training algorithm is proposed under SVM's decomposition framework by effectively integrating kernel caching, digest and shrinking policies and stopping conditions. Kernel cachi...
详细信息
A fast support vector machine (SVM) training algorithm is proposed under SVM's decomposition framework by effectively integrating kernel caching, digest and shrinking policies and stopping conditions. Kernel caching plays a key role in reducing the number of kernel evaluations by maximal reusage of cached kernel elements. Extensive experiments have been conducted on a large handwritten digit database MNIst to show that the proposed algorithm is much faster than Keerthi et al.'s improved SMO, about nine times. Combined with principal component analysis, the total training for ten one-against-the-rest classifiers on MNIst took less than an hour. Moreover, the proposed fast algorithm speeds up SVM training without sacrificing the generalization performance. The 0.6% error rate on MNIst test set has been achieved. The promising scalability of the proposed scheme paves a new way to solve more large-scale learning problems in other domains such as datamining.
The analysis of the typhoon is based on the manual patternrecognition of cloud patterns on meteorological satellite images by human experts, but this process may be unstable and unreliable, and we think could be impr...
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ISBN:
(纸本)354044016X
The analysis of the typhoon is based on the manual patternrecognition of cloud patterns on meteorological satellite images by human experts, but this process may be unstable and unreliable, and we think could be improved by taking advantage of both the large collection of past observations and the state-of-the-art machinelearning methods, among which kernel methods, such as support vector machines (SVM) and kernel PCA, are the focus of the paper. To apply the "learning-from-data" paradigm to typhoon analysis, we built the collection of more than 34,000 well-framed typhoon images to be used for spatio-temporal datamining of typhoon cloud patterns with the aim of discovering hidden and unknown regularities contained in large image databases. In this paper, we deal with the problem of visualizing and classifying typhoon cloud patterns using kernel methods. We compare preliminary results with baseline algorithms, such as principal component analysis and a k-NN classifier, and discuss experimental results with the future direction of research.
作者:
Petrou, MUniv Surrey
Sch Elect Engn Informat Technol & Math Guildford GU2 5XH Surrey England
learning in the context of a patternrecognition system is defined as the process that allows it to cope with real and ambiguous data. The various ways by which artificial decision systems operate are discussed in con...
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ISBN:
(纸本)3540665994
learning in the context of a patternrecognition system is defined as the process that allows it to cope with real and ambiguous data. The various ways by which artificial decision systems operate are discussed in conjunction with their learning aspects.
WISDOM++ is an intelligent document processing system that transforms a paper document into HTML/XML format. The main design requirement is adaptivity, which is realized through the application of machinelearning met...
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We describe the guidelines of a system for monitoring environmental risk situations. The system is based on datamining techniques and in particular classification trees working on the data base collected by the Itali...
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ISBN:
(纸本)3540665994
We describe the guidelines of a system for monitoring environmental risk situations. The system is based on datamining techniques and in particular classification trees working on the data base collected by the Italian National Hydro-geological Net. The gear of our application is to achieve a better discrimination among cases then that obtained by the system which is presently in use. The decision trees are evaluated and selected via a metric that takes a weighted account of the errors of different kinds.
Predictive models have been widely used long before the development of the new field that we call datamining. Expanding application demand for datamining of ever increasing data warehouses, and the need for understa...
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