With the development of the technology of C,41, it is widely used in our English teaching. So many people focus their attentions on the influences of CAI in different aspects. Therefore, the author tries to investigat...
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ISBN:
(纸本)9780769530901
With the development of the technology of C,41, it is widely used in our English teaching. So many people focus their attentions on the influences of CAI in different aspects. Therefore, the author tries to investigate the influences of CAI English teaching pattern on the Autonomous learning. in divided class instruction of independent college. As far as it is concerned, it is important for us to find out the essential influences of" divided class instruction" on students in independent college, thus to make the reform of our teaching pattern successfully.
Knowledge discovery and datamining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machinelearning, databases, statistics, knowledge acqu...
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ISBN:
(纸本)9780769530901
Knowledge discovery and datamining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machinelearning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. Knowledge discovery and datamining can be extremely beneficial for the field of Artificial Intelligence in many areas, such as industry, commerce, government, education and so on. The relation between Knowledge and datamining, and Knowledge Discovery in database (K-DD) process are presented in the paper. datamining theory, datamining tasks, datamining technology and datamining challenges are also proposed. This is an belief abstract for an invited talk at the workshop.
Outstanding success of CNN image classification affected using it as an instrument for time series classification. Powerful graph clustering methods have capabilities to come across entity relationships. In this study...
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Application of datamining for web log analysis has received significant attention in finding customers' behavioral pattern in e-commerce and learners' behavioral pattern in e-learning. While hit-counts indica...
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ISBN:
(纸本)9780769530901
Application of datamining for web log analysis has received significant attention in finding customers' behavioral pattern in e-commerce and learners' behavioral pattern in e-learning. While hit-counts indicate customers' interest in the product or purchasing behavior, a student's visits to a learning Management System (LMS) do not necessarily involve transfer of learning. Addressing such complexity in e-learning, this study analyzed students' log of a learning Management System (LMS) of two subjects at a university in Bangladesh, taught over six weeks duration. datamining and statistical tools have been used to rind relationships between students' LMS access behavior and overall performances. Results show that students having 'Low' access obtained poor grade, on campus access was higher than access from home. Background of students is very important for effective usage of web resources. Majority of the student considered LMS to be a quite helpful tool as teaching-learning method. Preparation and cleaning of the web-log files as well as application of datamining algorithms is important for learners' web usage analysis.
This paper proposes a new corrective learning algorithm and evaluates the performance by handwritten numeral recognition test. The algorithm generates a mirror image of a pattern which belongs to one class of a pair o...
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ISBN:
(纸本)3540423591
This paper proposes a new corrective learning algorithm and evaluates the performance by handwritten numeral recognition test. The algorithm generates a mirror image of a pattern which belongs to one class of a pair of confusing classes and utilizes it as a learningpattern of the other class. statistical patternrecognition techniques generally assume that the density function and the parameters of each class are only dependent on the sample of the class. The mirror image learning algorithm enlarges the learning sample of each class by mirror image patterns of other classes and enables us to achieve higher recognition accuracy with small learning sample. recognition accuracies of the minimum distance classifier and the projection distance method were improved from 93.17% to 98.38% and from 99.11% to 99.37% respectively in the recognition test for handwritten numeral database IPTP CD-ROM1 [1].
This paper investigates an interesting question of solving incremental learning problems using ensemble algorithms. The motivation is to help classifiers learn additional information from new batches of data increment...
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ISBN:
(纸本)9781424441990
This paper investigates an interesting question of solving incremental learning problems using ensemble algorithms. The motivation is to help classifiers learn additional information from new batches of data incrementally while preserving previously acquired knowledge. Experimental results show that the proposed dynamic weighting scheme can achieve better performance compared to the fixed weighting scheme on a variety of standard UCI benchmark datasets.
Analyzing multimedia data is a challenging problem due to the quantity and complexity of such data. mining for frequently recurring patterns is a task often ran to help discovering the underlying structure hidden in t...
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Mutual Information estimation is an important task for many datamining and machinelearning applications. In particular, many feature selection algorithms make use of the mutual information criterion and could thus b...
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ISBN:
(纸本)9789898425980
Mutual Information estimation is an important task for many datamining and machinelearning applications. In particular, many feature selection algorithms make use of the mutual information criterion and could thus benefit greatly from a reliable way to estimate this criterion. More precisely, the multivariate mutual information (computed between multivariate random variables) can naturally be combined with very popular search procedure such as the greedy forward to build a subset of the most relevant features. Estimating the mutual information (especially through density functions estimations) between high-dimensional variables is however a hard task in practice, due to the limited number of available data points for real-world problems. This paper compares different popular mutual information estimators and shows how a nearest neighbors-based estimator largely outperforms its competitors when used with high-dimensional data.
In many areas of patternrecognition and machinelearning, low dimensional data are often embedded in a high dimensional space. There have been many dimensionality reduction and manifold learning methods to discover t...
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ISBN:
(纸本)9781424441990
In many areas of patternrecognition and machinelearning, low dimensional data are often embedded in a high dimensional space. There have been many dimensionality reduction and manifold learning methods to discover the low dimensional representation from high dimensional data. Locality based manifold learning methods often rely on a distance metric between neighboring points. In this paper, we propose a new distance metric named relative distance, which is learned from the data and can better reflect the relative density. Combining the relative distance with Laplacian Eigenmaps (LE), we obtain a new algorithm called Relative Distance-based Laplacian Eigenmaps (RDLE) for nonlinear dimensionality reduction. Based on two different definitions of the relative distance, we give two variations of the RDLE. For efficient projection of out-of-sample data, we also present the linear version of RDLE, LRDLE. Experimental results on toy problems and real-world data demonstrate the effectiveness of our methods.
Various of manifold learning methods have been proposed to capture the intrinsic characteristic of nonlinear data. However, when confronting highly nonlinear data sets, existing algorithms may fail to discover the cor...
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ISBN:
(纸本)9781424441990
Various of manifold learning methods have been proposed to capture the intrinsic characteristic of nonlinear data. However, when confronting highly nonlinear data sets, existing algorithms may fail to discover the correct inner structure of data sets. In this paper, we proposed a new locality-based manifold learning method - Neighborhood Balance Embedding. The proposed method share the same 'neighborhood preserving' property with other manifold learning methods, however, it describe the local structure in a different way, which makes each neighborhood like as rigid balls, thus prevents the overlapping phenomenon which often happens when coping with highly nonlinear data. Experimental results on the data sets with high nonlinearity show good performances of the proposed method.
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