this work presents an image analysis framework driven by emerging evidence and constrained by the semantics expressed in an ontology. Human perception, apart from visual stimulus and patternrecognition, relies also o...
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ISBN:
(纸本)9783642030697
this work presents an image analysis framework driven by emerging evidence and constrained by the semantics expressed in an ontology. Human perception, apart from visual stimulus and patternrecognition, relies also on general knowledge and application context for understanding visual content in conceptual terms. Our work is an attempt to imitate this behavior by devising an evidence driven probabilistic, inference framework using ontologies and bayesian networks. Experiments conducted for two different image analysis, tasks showed improvement performance, compared to the case where computer vision techniques act isolated from any type of knowledge or context.
No-regret algorithms for online convex optimization are potent online learning tools and have been demonstrated to be successful in a wide-ranging number of applications. Considering affine and external regret, we, in...
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ISBN:
(纸本)9783642030697
No-regret algorithms for online convex optimization are potent online learning tools and have been demonstrated to be successful in a wide-ranging number of applications. Considering affine and external regret, we, investigate what happens when a set of no-regret learners (voters) merge their respective decisions in each learning iteration to a single, common one in form of a convex combination. We show that an agent (or algorithm) that executes this merged decision in each iteration of the online learning process and each time feeds back a copy of its own reward function to the voters, incurs sublinear regret itself. As a by-product, we obtain a simple method that allows us to construct new no-regret algorithms out of known ones.
In this paper we present a comparative analysis of two types of remote sensing satellite data by using the wavelet-based datamining techniques. the analyzed results reveal that the anomalous variations exist related ...
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ISBN:
(纸本)9783642030697
In this paper we present a comparative analysis of two types of remote sensing satellite data by using the wavelet-based datamining techniques. the analyzed results reveal that the anomalous variations exist related to the earthquakes. the methods studied in this work include wavelet transformations and spatial/temporal continuity analysis of wavelet maxima. these methods have been used to analyze the singularities of seismic anomalies in remote sensing satellite data, which are associated with file two earthquakes of Wenchuan and Pure recently occurred in China.
We face the problem of novelty detection from stream data, that is, the identification of new or unknown situations in an ordered sequence of objects which arrive on-line, at consecutive time points. We extend previou...
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ISBN:
(纸本)9783642030697
We face the problem of novelty detection from stream data, that is, the identification of new or unknown situations in an ordered sequence of objects which arrive on-line, at consecutive time points. We extend previous solutions by considering the case of objects modeled by multiple database relations. Frequent relational patterns are efficiently extracted at each time point, and a time window is used to filter out novelty patterns. An application of the proposed algorithm to the problem of detecting anomalies in network traffic is described and quantitative and qualitative results obtained by analyzing real stream of data collected from the firewall logs are reported.
datamining is the process of extracting interesting information from large sets of data. Outliers are defined as events that occur very infrequently. Detecting outliers before they escalate with potentially catastrop...
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ISBN:
(纸本)9783642030697
datamining is the process of extracting interesting information from large sets of data. Outliers are defined as events that occur very infrequently. Detecting outliers before they escalate with potentially catastrophic consequences is very important for various real life applications such as in the field of fraud detection, network robustness analysis, and intrusion detection. this paper presents a comprehensive analysis of three Outlier detection methods Extensible Markov Model (EMM), Local Outlier Factor (LOF) and LCS-Mine, where algorithm analysis shows the time complexity analysis and outlier detection accuracy. the experiments conducted with Ozone level Detection, IR video trajectories, and 1999 and 2000 DARPA DDoS datasets demonstrate that EMM outperforms both LOF and LSC-Mine in both time and outlier detection accuracy.
Prior knowledge about it problem domain can be utilized to bias Support Vector machines (SVMs) towards learning better hypothesis functions. To this end, a number of methods have been proposed that demonstrate improve...
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ISBN:
(纸本)9783642030697
Prior knowledge about it problem domain can be utilized to bias Support Vector machines (SVMs) towards learning better hypothesis functions. To this end, a number of methods have been proposed that demonstrate improved generalization performance after the application of domain knowledge;especially in the case of scarce training data. In this paper, we propose an extension to the Virtual Support vectors (VSVs) technique where only a subset of the Support vectors (SVs) is Utilized. Unlike previous methods, the Purpose here is to compensate for noise and uncertainty in the training data. Furthermore, we investigate the effect of domain knowledge not only oil the quality of the SVM model, but also Oil rules extracted from it: hence the learned pattern by the SVM. Results on five benchmark and one real life data sets show that domain knowledge can significantly improve boththe quality Of the SVM and the rules extracted from it.
Many applications require predictions with confidence. We are interested in Confidence machines which are algorithms that call provide some measure oil how confident they are that their Output is correct. Confidence M...
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ISBN:
(纸本)9783642030697
Many applications require predictions with confidence. We are interested in Confidence machines which are algorithms that call provide some measure oil how confident they are that their Output is correct. Confidence machines are quite general and there are many algorithms solving the problem of prediction with confidence. As predictors we consider Venn Probability machines and Conformal Predictors. Both of these algorithms rely oil all underlying algorithm for prediction and in this paper We use two simple algorithms, namely the Nearest Neighbours and Nearest Centroid algorithms. Our aim is to provide some guidelines on how to choose the Most Suitable algorithm for a practical application where confidence is needed.
Many data sets derived from the web are large, high-dimensional, sparse and have a Zipfian distribution of both classes and features. On such data sets, current scalable clustering methods such as streaming clustering...
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ISBN:
(纸本)9783642030697
Many data sets derived from the web are large, high-dimensional, sparse and have a Zipfian distribution of both classes and features. On such data sets, current scalable clustering methods such as streaming clustering suffer from fragmentation. where large classes are incorrectly divided into many smaller clusters. and computational efficiency drops significantly. We present a new clustering algorithm based on connected components that addresses these issues and so works well oil web-type data.
the new approach of relevant feature selection in machinelearning is proposed for the case of ordered features. Feature selection and regularization of decision rule are combined in a single procedure. the selection ...
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ISBN:
(纸本)9783642030697
the new approach of relevant feature selection in machinelearning is proposed for the case of ordered features. Feature selection and regularization of decision rule are combined in a single procedure. the selection of features is realized by introducing weight coefficients, characterizing degree of relevance of respective feature. A priori information about feature ordering is taken into account in the form of quadratic penalty or in the form of absolute Value penalty on the difference of weight coefficients of neighboring features. Study of a penalty function in the form of absolute value shows computational complexity Of Such formulation. the effective method of solution is proposed. the brief survey of authors early papers. the mathematical frameworks, and experimental results are provided.
this paper addresses the problem of automatically learningthe title metadata from HTML documents. the objective, is to help indexing Web resources that are poorly annotated. Other works proposed similar objectives, b...
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ISBN:
(纸本)9783642030697
this paper addresses the problem of automatically learningthe title metadata from HTML documents. the objective, is to help indexing Web resources that are poorly annotated. Other works proposed similar objectives, but they considered only titles in text format . In this paper we propose a general learning schema that allows learning textual titles based on style information and image format titles based on image properties. We construct features from automatically annotated pages harvested from the Web;this paper details the corpus creation method as well as the information extraction techniques. Based oil these features. learning algorithms, such as Decision Trees and Random Forest algorithms are applied achieving good results despite the heterogeneity of our corpus, we also show that, combining both methods can induce better performance.
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