the proposition of adaptive selection of rule quality measures during rules induction is presented in the paper. In the applied algorithm the measures decide about a form of elementary conditions in a rule premise and...
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ISBN:
(纸本)9783642218804
the proposition of adaptive selection of rule quality measures during rules induction is presented in the paper. In the applied algorithm the measures decide about a form of elementary conditions in a rule premise and monitor a pruning process. An influence of filtration algorithms on classification accuracy and a number of obtained rules is also presented. the analysis has been done on twenty one benchmark data sets.
this paper investigates the performance of hidden Markov models (HMMs) for handwriting recognition. the Segmental K-Means algorithm is used for updating the transition and observation probabilities, instead of the Bau...
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ISBN:
(纸本)9783642217869
this paper investigates the performance of hidden Markov models (HMMs) for handwriting recognition. the Segmental K-Means algorithm is used for updating the transition and observation probabilities, instead of the Baum-Welch algorithm. Observation probabilities are modelled as multi-variate Gaussian mixture distributions. A deterministic clustering technique is used to estimate the initial parameters of an HMM. Bayesian information criterion (BIC) is used to select the topology of the model. the wavelet transform is used to extract features from a grey-scale image, and avoids binarization of the image.
the paper describes the method of extraction of two-word domain terms combining their features. the features are computed from three sources: the occurrence statistics in a domain-specific text collection, the statist...
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ISBN:
(纸本)9783642217869
the paper describes the method of extraction of two-word domain terms combining their features. the features are computed from three sources: the occurrence statistics in a domain-specific text collection, the statistics of global search engines, and a domain-specific thesaurus. the evaluation of the approach is based on the terminology of manually created thesauri. We show that the use of multiple features considerably improves the automatic extraction of domain-specific terms. We compare the quality of the proposed method in two different domains.
Problem of change detection of remotely sensed images using insufficient labeled patterns is the main topic of present work. Here, semi-supervised learning is integrated with an unsupervised context-sensitive change d...
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ISBN:
(纸本)9783642217869
Problem of change detection of remotely sensed images using insufficient labeled patterns is the main topic of present work. Here, semi-supervised learning is integrated with an unsupervised context-sensitive change detection technique based on modified self-organizing feature map (MSOFM) network. In this method, training of the MSOFM is performed iteratively using unlabeled patterns along with a few labeled patterns. A method has been suggested to select unlabeled patterns for training. To check the effectiveness of the proposed methodology, experiments are carried out on two multitemporal remotely sensed images. Results are found to be encouraging.
Student modeling is one of the key factors that affects automated tutoring systems in making instructional decisions. A student model is a model to predict the probability of a student making errors on given problems....
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ISBN:
(纸本)9789038625379
Student modeling is one of the key factors that affects automated tutoring systems in making instructional decisions. A student model is a model to predict the probability of a student making errors on given problems. A good student model that matches with student behavior patterns often provides useful information on learning task difficulty and transfer of learning between related problems, and thus often yields better instruction. Manual construction of such models usually requires substantial human effort, and may still miss distinctions in content and learningthat have important instructional implications. In this paper, we propose an approach that automatically discovers student models using a state-of-art machinelearning agent, SimStudent. We show that the discovered model is of higher quality than human-generated models, and demonstrate how the discovered model can be used to improve a tutoring system's instruction strategy.
this paper describes a non-blind, imperceptible and highly robust hybrid Medical Image Watermarking (MIW) technique for a range of medical data management issues. the method simultaneously addresses medical informatio...
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ISBN:
(纸本)9783642217869
this paper describes a non-blind, imperceptible and highly robust hybrid Medical Image Watermarking (MIW) technique for a range of medical data management issues. the method simultaneously addresses medical information security, content authentication, safe archiving and controlled access retrieval. We propose the use of Contourlet Transform (CLT) followed by the Discrete Cosine Transform (DCT) to achieve higher robustness and imperceptibility. Experimental results and performance comparisons confirm the effectiveness and efficiency of the proposed scheme.
In this paper, we present a consumption patternrecognition system based on SVM. It can produce an optimized classification pattern using SVM algorithm and use the pattern to predict consumer behaviors. In this system...
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the proceedings contain 67 papers. the topics discussed include: on exploration and mining of data in educational practice;factorization models for forecasting student performance;analyzing participation of students i...
ISBN:
(纸本)9789038625379
the proceedings contain 67 papers. the topics discussed include: on exploration and mining of data in educational practice;factorization models for forecasting student performance;analyzing participation of students in online courses using social network analysis techniques;a machinelearning approach for automatic student model discovery;student translations of natural language into logic: the grade grinder translation corpus release 1.0;instructional factors analysis: a cognitive model for multiple instructional interventions;the simple location heuristic is better at predicting students changes in error rate over time compared to the simple temporal heuristic;items, skills, and transfer models: which really matters for student modeling?;avoiding problem selection thrashing with conjunctive knowledge tracing;and less is more: improving the speed and prediction power of knowledge tracing by using less data.
In modern collaborative filtering applications initial data are typically very large (holding millions of users and items) and come in real time. In this case only incremental algorithms are practically efficient. In ...
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ISBN:
(纸本)9783642217869
In modern collaborative filtering applications initial data are typically very large (holding millions of users and items) and come in real time. In this case only incremental algorithms are practically efficient. In this paper a new algorithm based on the symbiosis of Incremental Singular Value Decomposition (ISVD) and Generalized Hebbian Algorithm (GHA) is proposed. the algorithm does not require to store the initial data matrix and effectively updates user/item profiles when a new user or a new item appears or a matrix cell is modified. the results of experiments show how root mean square error (RMSE) depends on the number of algorithm's iterations and data amount.
Semantic network is an information model of knowledge domain. Objects and their relations are specified with an attributed graph. Multistripe layout is suitable for visualization of relations incident to the selected ...
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ISBN:
(纸本)9783642218804
Semantic network is an information model of knowledge domain. Objects and their relations are specified with an attributed graph. Multistripe layout is suitable for visualization of relations incident to the selected set of objects. the method provides a compact drawing that is guaranteed to avoid link crossings and label overlaps for objects and relations of corresponding subnetwork. In this paper we describe a common scheme of the multistripe layout approach and propose the way of visualization of semantic network fragments. these fragments may contain additional relations and objects in comparison with subnetworks considered earlier.
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