the task of extracting knowledge from text is an important research problem for information processing and document understanding. Approaches to capture the semantics of picture objects in documents constitute subject...
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this paper describes an interactive graphical user interface tool called Visual Apriori that can be used to study two famous frequent itemset generation algorithms, namely, Apriori and Eclat. Understanding the functio...
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ISBN:
(纸本)0769524958
this paper describes an interactive graphical user interface tool called Visual Apriori that can be used to study two famous frequent itemset generation algorithms, namely, Apriori and Eclat. Understanding the functional behavior of these two algorithms is critical for students taking a datamining course;and Visual Apriori provides a hands-on environment for doing so. Visual Apriori relies on active participation from the user where one inputs a transactional database and the tool produces a tree-based frequent itemset generation animation for the algorithm chosen. Visual Apriori provides an effortless learning experience by featuring user-friendly and easy to understand controls.
this study investigates the effectiveness of probability forecasts output by standard machinelearning techniques (Neural Network, C4.5, K-Nearest Neighbours, Naive Bayes, SVM and HMM) when tested on time series datas...
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ISBN:
(纸本)3540287574
this study investigates the effectiveness of probability forecasts output by standard machinelearning techniques (Neural Network, C4.5, K-Nearest Neighbours, Naive Bayes, SVM and HMM) when tested on time series datasets from various problem domains, Raw data was converted into a pattern classification problem using a sliding window approach, and the respective target prediction was set as some discretised future value in the time series sequence. Experiments were conducted in the online learning setting to model the way in which time series data is presented. the performance of each learner's probability forecasts was assessed using ROC curves, square loss, classification accuracy and Empirical Reliability Curves (ERC) [1]. Our results demonstrate that effective probability forecasts can be generated on time series data and we discuss the practical implications of this.
this paper proposes a learning approach for discovering the semantic structure of web pages. the task includes partitioning the text on a web page into information blocks and identifying their semantic categories. We ...
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ISBN:
(纸本)0769524206
this paper proposes a learning approach for discovering the semantic structure of web pages. the task includes partitioning the text on a web page into information blocks and identifying their semantic categories. We employed two machinelearning techniques, Adaboost and SVMs, to learn from a labeled web page corpus. We evaluated our approach on general web pages from the World Wide Web and obtained encouraging results. this work can be beneficial to a number of web-driven applications such as search engines, web-based question answering, web-based datamining as well as voice enabled web navigation.
the load forecasting of the artificial neural network based on datamining technology advanced in this paper has improved the forecast precision in two aspects. the first one is to carve load history up different weat...
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ISBN:
(纸本)0780390911
the load forecasting of the artificial neural network based on datamining technology advanced in this paper has improved the forecast precision in two aspects. the first one is to carve load history up different weather character and take out many load history as the same as the weather character of the forecast load and forecast the load based on the datamining technology. the other is to optimize the structure of neural network and get the satisfactory forecast result. Since the model considered the influence of weather factor and optimized of the structure of neural network, this method improve the load forecast precision greatly.
the work presented in this paper is part of the cooperative research project AUTO-OPT carried out by twelve partners from the automotive industries. One major work package concerns the application of datamining metho...
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In this paper we present a method to cluster large datasets that change over time using incremental learning techniques. the approach is based on the dynamic representation of clusters that involves the use of two set...
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Face recognition is a challenging visual classification task, especially when the lighting conditions can not be controlled. In this paper, we present an automatic face recognition system in the near infrared (IR) spe...
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Feature selection method for text classification based on information gain ranking, improved by removing redundant terms using mutual information measure and inclusion index, is proposed. We report an experiment to st...
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An efficient low-level word image representation plays a crucial role in general cursive word recognition. this paper proposes a novel representation scheme, where a word image can be represented as two sequences of f...
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