Assessing the similarity between objects is a prerequisite for many datamining techniques. this paper introduces a novel approach to learn distance functions that maximizes the clustering of objects belonging to the ...
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Network security is one of the major concerns of modern organizations. Precise and accurate detection of attacks need modern tools which utilize past data and current trends to evaluate the situation under considerati...
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this work suggests an unsupervised fuzzy clustering algorithm based on the concept of participatory learning introduced by Yager in the nineties. the performance of the algorithm is verified with synthetic data sets a...
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this work suggests an unsupervised fuzzy clustering algorithm based on the concept of participatory learning introduced by Yager in the nineties. the performance of the algorithm is verified with synthetic data sets and withthe well-known Iris data. In both circumstances the participatory learning algorithm determines the expected number of clusters and the corresponding cluster centers successfully. Comparisons with Gustafson-Kessel (GK) and modified fuzzy k-means (MFKM) are included to show the effectiveness of the participatory approach in data clustering
this paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines ...
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this paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines (SVM) and minimax probability machines (MPM). Within the STL framework, many conventional learningmachines can be generalized to take n/sup th/-order tensors as inputs. We also study the applications of tensors to learningmachine design and feature extraction by linear discriminant analysis (LDA). Our method for tensor based feature extraction is named the tenor rank-one discriminant analysis (TR1DA). these generalized algorithms have several advantages: 1) reduce the curse of dimension problem in machinelearning and datamining; 2) avoid the failure to converge; and 3) achieve better separation between the different categories of samples. As an example, we generalize MPM to its STL version, which is named the tensor MPM (TMPM). TMPM learns a series of tensor projections iteratively. It is then evaluated against the original MPM. Our experiments on a binary classification problem show that TMPM significantly outperforms the original MPM.
In recent years, machinelearning and datamining fields have found a successful application area in the field of DNA microarray technology. Gene expression profiles are composed of thousands of genes at the same time...
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the following topics are dealt with: image, speech and signal processing; software engineering; operating system and compiler; datamining and knowledge discovery; machinelearning, natural language, and neural networ...
the following topics are dealt with: image, speech and signal processing; software engineering; operating system and compiler; datamining and knowledge discovery; machinelearning, natural language, and neural networks; software architecture; Web and multimedia; voice over IP; cryptography and network security; wireless networks; parallel and distributed computing; adhoc networks and routing protocols; learning system; learning system; meeting system; positioning and routing; and QOS and energy efficiency.
Clustering is crucial to many applications in patternrecognition, datamining, and machinelearning. Evolutionary techniques have been used with success in clustering, but most suffer from several shortcomings. We fo...
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ISBN:
(纸本)3540223436
Clustering is crucial to many applications in patternrecognition, datamining, and machinelearning. Evolutionary techniques have been used with success in clustering, but most suffer from several shortcomings. We formulate requirements for efficient encoding, resistance to noise, and ability to discover the number of clusters automatically.
the support vector machine (SVM) is considered here in the context of pattern classification, the emphasis is on the soft margin classifier which uses regularization to handle non-separable learning samples. We presen...
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ISBN:
(纸本)0769521428
the support vector machine (SVM) is considered here in the context of pattern classification, the emphasis is on the soft margin classifier which uses regularization to handle non-separable learning samples. We present an SVM parameter estimation algorithm that first identifies a subset of,the learning samples that we call the support set and then determines not only the weights of the classifier but, also the hyperparameter that controls the influence of the regularizing penalty term, on basis thereof. We provide numerical results using several data sets from the public domain.
We present a new unsupervised learning technique for the discovery of temporal clusters in large data sets. Our method performs hierarchical decomposition of the data to find structure at many levels of detail and to ...
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ISBN:
(纸本)0769521223
We present a new unsupervised learning technique for the discovery of temporal clusters in large data sets. Our method performs hierarchical decomposition of the data to find structure at many levels of detail and to reduce the overall computational cost of pattern discovery. We present a comparison to related methods on synthetic data sets and on real gestural and pedestrian flow data.
the C4.5 Decision Tree and Naive Bayes learners are known to produce unreliable probability forecasts. We have used simple Binning [11] and Laplace Transform [2] techniques to improve the reliability of these learners...
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ISBN:
(纸本)0769521428
the C4.5 Decision Tree and Naive Bayes learners are known to produce unreliable probability forecasts. We have used simple Binning [11] and Laplace Transform [2] techniques to improve the reliability of these learners and compare their effectiveness withthat of the newly developed Venn Probability machine (VPM) meta-learner [9]. We assess improvements in reliability using loss functions, Receiver Operator Characteristic (ROC) curves and Empirical Reliability Curves (ERC). the VPM outperforms the simple techniques to improve reliability, although at the cost of increased computational intensity and slight increase in error rate. these trade-offs are discussed.
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