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Aiming at the problem of service performance modelling, this paper uses ANN to establish the mapping relationships between virtual machine resource status and cloud service performance, and proposes an I-ABC-ELM method to train ANN. For the stability problems of the ELM training, this paper proposes the use of I-ABC algorithm to optimize the input layer weight matrix and hidden layer bias in ELM....
To contribute software testing, and save testing costs, a wide range of machine learning approachs have been studied to predict defects in software modules. Unfortunately, the imbalanced nature of this type of data increases the learning difficulty of such a task. In this paper, we present UCRF, a method based on undersampling technique and conditional random field (CRF) for software defect prediction...
For some time past, support vector machine (SVM) has been generally used in pattern recognition, classification and prediction. However, in traditional SVM arithmetic, various kernels cannot recognize the importance of the feature vector properties, so the prediction accuracy seems to be unsatisfactory. For purpose of optimizing the problem, this paper proposes an modified support vector regression(SVR)...
The MODIS vegetation continuous fields (VCF) product has a percent tree cover layer; hence it could potentially be used to detect hotspots of deforestation and forest degradation, if data accuracy is high. This paper assesses the accuracy of the VCF percent tree cover layer by comparing it with land cover maps in two areas in Mexico. Specifically, we assess whether it can (1) differentiate forest...
The discriminant analysis for Similar Handwritten Chinese Character Recognition (SHCR) is essential for the improvement of handwritten Chinese character recognition performance. In this paper, a new manifold based subspace learning algorithm, Discriminative Locality Alignment (DLA), is introduced into SHCR. Experimental results demonstrate that DLA is consistently superior to LDA (Linear Discriminant...
In this paper, we propose a novel region grouping approach to shape matching. It is proposed as an alternative region based approach to the traditional edge based shape matching using distance transforms. It has the advantage of obtaining a higher detection rate and obtaining meaningful object segmentation simultaneously. Each image is first segmented into image regions, and possible matches are found...
For a finite set of points lying on a lower dimensional manifold embedded in a high-dimensional data space, algorithms have been developed to study the manifold structure. However, many algorithms will fail if data are noisy. We propose a method based on Gaussian process latent variable models for manifold denoising with the following advantages: (1), it is probabilistic, which naturally handles noise...
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