The network learning behavior intelligence analysis system can collect the information of learner's psychology, behavior, methods and effectiveness in the learning process, and classify;learners by using the ID3 a...
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ISBN:
(纸本)9780769530901
The network learning behavior intelligence analysis system can collect the information of learner's psychology, behavior, methods and effectiveness in the learning process, and classify;learners by using the ID3 algorithm based on the internal factors and personality characteristics of learners that influence the learning effect. In order to correct the shortcomings that the ID3 algorithm more inclined to the attributes that have more values in the classification process, we introduce user interest, which used to distinguish the dependence between different information attributes. At the same time, we introduce parameters to reduce the redundancy between attributes, and accelerate the pace of information entropy reducing, then construct a general, expandable senior vocational student model in the intelligence-learning environment.
The paper presents a flexible framework for the online evaluation of collaborative project-oriented e-learning platforms. The framework was developed for the evaluation of the COOPER platform [1], but it is flexible e...
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The paper presents a flexible framework for the online evaluation of collaborative project-oriented e-learning platforms. The framework was developed for the evaluation of the COOPER platform [1], but it is flexible enough to be used for the evaluation of other collaborative platforms. The evaluation is based on questionnaires and on logs collected during the platform use. Here we focus on the questionnaire-based evaluation part, which was developed with the same WebRatio model-driven development tool that was used for the COOPER platform. This solution assures a uniform implementation and interface with the whole platform. statistical analysis of the results is combined with other methods such as social networks evaluation (see [14] for details), thus offering a complex evaluation framework. Some evaluation results obtained in experiments developed in two use cases are presented along with conclusions derived from the experiments.
This paper presents an SVM-based learning system for information extraction (IE). One distinctive feature of our system is the use of a variant of the SVM, the SVM with uneven margins, which is particularly helpful fo...
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ISBN:
(纸本)3540290737
This paper presents an SVM-based learning system for information extraction (IE). One distinctive feature of our system is the use of a variant of the SVM, the SVM with uneven margins, which is particularly helpful for small training datasets. In addition, our approach needs fewer SVM classifiers to be trained than other recent SVM-based systems. The paper also compares our approach to several state-of-the-art systems (including rule learning and statisticallearning algorithms) on three IE benchmark datasets: CoNLL-2003, CMU seminars, and the software jobs corpus. The experimental results show that our system outperforms a recent SVM-based system on CoNLL-2003, achieves the highest score on eight out of 17 categories on the Jobs corpus, and is second best on the remaining nine.
In classification problems, machinelearning algorithms often make use of the assumption that (dis)similar inputs lead to (dis)similar outputs. In this case, two questions naturally arise: what does it mean for two in...
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ISBN:
(纸本)3540290737
In classification problems, machinelearning algorithms often make use of the assumption that (dis)similar inputs lead to (dis)similar outputs. In this case, two questions naturally arise: what does it mean for two inputs to be similar and how can this be used in a learning algorithm? In support vector machines, similarity between input examples is implicitly expressed by a kernel function that calculates inner products in the feature space. For numerical input examples the concept of an inner product is easy to define, for discrete structures like sequences of symbolic data however these concepts are less obvious. This article describes an approach to SVM learning for symbolic data that can serve as an alternative to the bag-of-words approach under certain circumstances. This latter approach first transforms symbolic data to vectors of numerical data which are then used as arguments for one of the standard kernel functions. In contrast, we will propose kernels that operate on the symbolic data directly.
In many data-driven machinelearning problems it is useful to consider the data as generated from a set of unknown (latent) generators or sources. The observations we make are then taken to be related to these sources...
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ISBN:
(纸本)3540290737
In many data-driven machinelearning problems it is useful to consider the data as generated from a set of unknown (latent) generators or sources. The observations we make are then taken to be related to these sources through some unknown functionaility. Furthermore, the (unknown) number of underlying latent sources may be different to the number of observations and hence issues of model complexity plague the analysis. Recent developments in Independent Component Analysis (ICA) have shown that, in the case where the unknown function linking sources to observations is linear, data decomposition may be achieved in a mathematically elegant manner. In this paper we extend the general ICA paradigm to include a very flexible source model and prior constraints and argue that for particular biomedical signal processing problems (we consider EEG analysis) we require the constraint of positivity in the mixing process.
We discuss two kernel based learningmethods, namely the Regularization Networks (RN) and the Radial Basis Function (RBF) Networks. The RNs are derived from the regularization theory, they had been studied thoroughly ...
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ISBN:
(纸本)3540290737
We discuss two kernel based learningmethods, namely the Regularization Networks (RN) and the Radial Basis Function (RBF) Networks. The RNs are derived from the regularization theory, they had been studied thoroughly from a function approximation point of view, and they posses a sound theoretical background. The RBF networks represent a model of artificial neural networks with both neuro-physiological and mathematical motivation. In addition they may be treated as a generalized form of Regularization Networks. We demonstrate the performance of both approaches on experiments, including both benchmark and real-life learning tasks. We claim that RN and RBF networks are comparable in terms of generalization error, but they differ with respect to their model complexity. The RN approach usually leads to solutions with higher number of base units, thus, the RBF networks can be used as a 'cheaper' alternative. This allows to utilize the RBF networks in modeling tasks with large amounts of data, such as time series prediction or semantic web classification.
Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. There is good reason to believe that predictions from these different classes of algorithms could be use...
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ISBN:
(纸本)3540290737
Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. There is good reason to believe that predictions from these different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks, rules sets and support vector machines on predictions from 12 key algorithms. Furthermore, we use a 'window' of consecutive results in the input vector in order to contextualise the neighbouring results. Moreover, we improve the classification result with the aid of under- and over-sampling techniques. We find that support vector machines outperform each of the original individual algorithms and other classifiers employed in this work with both type of inputs, in that they maintain a better tradeoff between recall and precision.
In corporate data mining applications, cost-sensitive learning is firmly established for predictive classification algorithms. Conversely, data mining methods for regression and time series analysis generally disregar...
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ISBN:
(纸本)1595932089
In corporate data mining applications, cost-sensitive learning is firmly established for predictive classification algorithms. Conversely, data mining methods for regression and time series analysis generally disregard economic utility and apply simple accuracy measures. methods from statistics and computational intelligence alike minimise a symmetric statistical error, such as the sum of squared errors, to model ordinary least squares predictors. However, applications in business elucidate that real forecasting problems contain non-symmetric errors. The costs arising from over- versus underprediction are dissimilar for errors of identical magnitude, requiring an ex-post correction of the prediction to derive valid decisions. To reflect this, an asymmetric cost function is developed and employed as the objective function for neural network training, deriving superior forecasts and a cost efficient decision. Experimental results for a business scenario of inventory-levels are computed using a multilayer perceptron trained with different objective functions, evaluating the performance in competition to statistical forecasting methods. Copyright 2005 ACM.
In this paper, we introduce a new method (SVM_2K) which amalgamates the capabilities of the Support Vector machine (SVM) and Kernel Canonical Correlation Analysis (KCCA) to give a more sophisticated combination rule t...
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