This contribution introduces a software framework enabling researchers to develop real-time patternrecognition and sensor fusion applications in an abstraction level above that of common programming languages in orde...
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There is no single generic kernel that suits all estimation tasks. Kernels that are learnt from the data are known to yield better classification. The coefficients of the optimal kernel that maximizes the class separa...
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ISBN:
(纸本)9783642111631
There is no single generic kernel that suits all estimation tasks. Kernels that are learnt from the data are known to yield better classification. The coefficients of the optimal kernel that maximizes the class separability in the empirical feature space had been previously obtained by a gradient-based procedure. In this paper, we show how these coefficients can be learnt from the data by simply solving a generalized eigenvalue problem. Our approach yields a significant reduction in classification errors on selected UCI. benchmarks.
Integration methods for ensemble learning can use two different approaches: combination or selection. The combination approach (also called fusion) consists on the combination of the predictions obtained by different ...
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ISBN:
(纸本)9783642030697
Integration methods for ensemble learning can use two different approaches: combination or selection. The combination approach (also called fusion) consists on the combination of the predictions obtained by different models in the ensemble to obtain the final ensemble predication. The selection approach selects one (or more) models from the ensemble according to the prediction performance of these models on similar data from the validation set. Usually, the method to select similar data is the k-nearest neighbors with the Euclidean distance. In this paper we discuss other approaches to obtain similar data for the regression problem. We show that using similarity measures according to the target values improves results. We also show that selecting dynamically several models for the prediction task increases prediction accuracy comparing to the selection of just one model.
The paper is about speeding-up the k-means clustering method which processes the data in a faster pace, but;produces the same clustering result as the k-means method. We present a prototype based method for this where...
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ISBN:
(纸本)9783642111631
The paper is about speeding-up the k-means clustering method which processes the data in a faster pace, but;produces the same clustering result as the k-means method. We present a prototype based method for this where prototypes are derived using the leaders clustering method. Along with prototypes called leaders some additional information is also preserved which enables in deriving the k means. Experimental study is done to compare the proposed method with recent similar methods which are mainly based on building an index over the data-set.
In this second part of the paper, we compare the cluster quality of K-means, GA K-means, rough K-means, GA rough K-means and GA rough K-medoid algorithms. We experimented with a real world data set, and a standard dat...
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ISBN:
(纸本)9783642111631
In this second part of the paper, we compare the cluster quality of K-means, GA K-means, rough K-means, GA rough K-means and GA rough K-medoid algorithms. We experimented with a real world data set, and a standarddata set using total within cluster variation, precision and execution time as the measures of comparison.
Combining advanced datamining and biomedical technologies to discovering new drug is an active research field nowadays. In this paper, we collect a herbal compounds for rheum database by searching about 150 prescript...
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ISBN:
(纸本)9781424429011
Combining advanced datamining and biomedical technologies to discovering new drug is an active research field nowadays. In this paper, we collect a herbal compounds for rheum database by searching about 150 prescriptions in ancient herbal document. 255 herbal compounds are included for their combinations to heal rheum. Our aim is to discover potentially new herbal compound in the database. We present the unsupervised pattern discovery algorithm to allocate the herbal compounds into different cluster in a self-organized way and obtain 42 clusters, some of which fully accord with Chinese medicine theory and the other can be considered as the potential new drug, which need to be validated by pharmacology further. We also present an executable and effect strategy for further experiments. We conclude that datamining methods, especially, unsupervised learning method, can be taken as a new technique to discovering new drugs.
Although a vast majority of inductive learning algorithms has been developed for handling of the concept drifting data streams, especially the ones in Wine of ensemble classification models, few of them could adapt to...
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ISBN:
(纸本)9783642030697
Although a vast majority of inductive learning algorithms has been developed for handling of the concept drifting data streams, especially the ones in Wine of ensemble classification models, few of them could adapt to Hie detection oil the different types of concept drifts from noisy streaming data in a demand on overheads of time and space. Motivated by this, a new classification algorithm for Concept drifting Detection based on an ensembling model of Random Decision Trees (called CDrdT) is proposed in this paper. Extensive studies with synthetic and real streaming dam demonstrate that in comparison to several classification algorithms for concept drifting data streams, CDrdT not only could effectively and efficiently detect the potential concept changes in the noisy data streams, but also performs much better oil the abilities of runtime and space with an improvement in predictive accuracy. Thus, our proposed algorithm provides a significant reference to the classification for concept drifting data streams with noise in a light, weight way.
Sequential patternmining is new trend in datamining domain with many useful applications, especially commercial application but it also results surprised effect in adaptive learning. Suppose there is an adaptive e-l...
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ISBN:
(纸本)9781424423453
Sequential patternmining is new trend in datamining domain with many useful applications, especially commercial application but it also results surprised effect in adaptive learning. Suppose there is an adaptive e-learning website, a student access learning material / do exercises relating domain concepts in sessions. His learning sequences which are lists of concepts accessed after total study sessions construct the learning sequence database S. S is mined to find the sequences which are expected to be learned frequently or preferred by student. Such sequences called sequential patterns are use to recommend appropriate concepts / learning objects to students in his next visits. It results in enhancing the quality of adaptive learning system. This process is sequential patternmining. In paper, we also suppose an approach to break sequential pattern s=square c1, c2,..., cm square into association rules including left-hand and right-hand in form ci -> cj. Left-hand is considered as source concept, right-hand is treated as recommended concept available to students.
While for many problems in medicine classification models are being developed, Bayesian network classifiers do not seem to have become is widely accepted within the medical community as logistic regression models. We ...
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ISBN:
(纸本)9783642030697
While for many problems in medicine classification models are being developed, Bayesian network classifiers do not seem to have become is widely accepted within the medical community as logistic regression models. We compare first-order logistic regression and naive Bayesian classification in the domain of reproductive medicine and demonstrate that the two techniques can result in models of comparable performance. For Bayesian network classifiers to become more widely accepted within the medical community, we feel that they should be better aligned with their context of application. We describe how to incorporate well-known concepts of clinical relevance in the process Of Constructing and evaluating Bayesian network classifiers to achieve Such an alignment.
Evaluation is a key part while proposing a new model. To evaluate models of visual saliency, one needs to compare the model's Output with salient locations in an image. This paper proposes an approach to find out ...
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ISBN:
(纸本)9783642111631
Evaluation is a key part while proposing a new model. To evaluate models of visual saliency, one needs to compare the model's Output with salient locations in an image. This paper proposes an approach to find out the salient locations, i.e., groundtruth for experiments with visual saliency models. It is found that the proposed human hand-eye coordination based technique can be an alternative to costly human pupil-tracking based systems. Moreover, an evaluation metric is also proposed that suits the necessity of the saliency models.
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