Search

To better carry out Massive Open Online Courses (MOOC) teaching evaluation and improve teaching effect, firstly, a teaching decision support system with evaluation function is designed by analyzing the actual situation of the college. Secondly, the decision tree data mining algorithm is introduced in the subsystem of student score analysis and evaluation. Finally, the decision tree model of student score analysis evaluation is constructed according to the decision tree algorithm. Through the practical exploration of applying the decision tree algorithm to the MOOC teaching evaluation management system of higher vocational colleges, it is found that the application of data mining technology to the construction of digital campus is not only reflected in the theoretical feasibility, but also reflected in its technical feasibility. [ABSTRACT FROM AUTHOR]

It is a challenging problem to preserve friendly-correlations between individuals when publishing social network data. To alleviate this problem, uncertain graph has been presented recently. The main idea of uncertain graph is converting an original graph into an uncertain form, where the friendly-correlations of the graph are associated with probabilities. However, the existing methods of uncertain graph lack rigorous guarantees of privacy and rely on the assumption of adversary's knowledge. In this paper, we introduced a general model for constructing uncertain graphs. Then, we proposed an Uncertain Graph based on Differential Privacy algorithm (UGDP algorithm) under the general model which provides a rigorous privacy guarantee against powerful adversaries, and we define a new metric to measure privacy for different algorithms. Finally, we evaluate some uncertain algorithms in privacy and utility, the result shows that UGDP algorithm satisfies edge-differential privacy and the data utility is acceptable. The conclusions are that the UGDP algorithm has better privacy preserving than the (k, ε)-obfuscation algorithm, and better data utility than the RandWalk algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the relationship between the concept lattice and quotient space by granularity. A new framework of knowledge representation - granular quotient space - is constructed and it demonstrates that concept lattice classing is linked to quotient space. The covering of the formal context is firstly given based on this granule, then the granular concept lattice model and its construction are discussed on the sub-context which is formed by the granular classification set. We analyze knowledge reduction and give the description of granular entropy techniques, including some novel formulas. Lastly, a concept lattice constructing algorithm is proposed based on multi-granular feature selection in quotient space. Examples and experiments show that the algorithm can obtain a minimal reduct and is much more efficient than classical incremental concept formation methods. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of speaker recognition in noisy conditions. A new approach called nonnegative tensor principal component analysis (NTPCA) with sparse constraint is proposed for speech feature extraction. We encode speech as a general higher-order tensor in order to extract discriminative features in spectrotemporal domain. Firstly, speech signals are represented by cochlear feature based on frequency selectivity characteristics at basilar membrane and inner hair cells; then, low-dimension sparse features are extracted by NTPCA for robust speaker modeling. The useful information of each subspace in the higher-order tensor can be preserved. Alternating projection algorithm is used to obtain a stable solution. Experimental results demonstrate that our method can increase the recognition accuracy specifically in noisy environments. [ABSTRACT FROM AUTHOR]

We introduce an algorithm that simultaneously estimates a classification function as well as its gradient in the supervised learning framework. The motivation for the algorithm is to find salient variables and estimate how they covary. An efficient implementation with respect to both memory and time is given. The utility of the algorithm is illustrated on simulated data as well as a gene expression data set. An error analysis is given for the convergence of the estimate of the classification function and its gradient to the true classification function and true gradient. [ABSTRACT FROM AUTHOR]

This paper considers the regularized learning algorithm associated with the least-square loss and reproducing kernel Hilbert spaces. The target is the error analysis for the regression problem in learning theory. A novel regularization approach is presented, which yields satisfactory learning rates. The rates depend on the approximation property and on the capacity of the reproducing kernel Hilbert space measured by covering numbers. When the kernel is C∞ and the regression function lies in the corresponding reproducing kernel Hilbert space, the rate is mζ with ζ arbitrarily close to 1, regardless of the variance of the bounded probability distribution. [ABSTRACT FROM AUTHOR]

This paper studies support vector machine classification algorithms. We analyze the 1-norm soft margin classifier. The consistency is considered in two forms. When the regularization error decays to zero, the Bayes-risk consistency is proved and learning rates are derived by means of techniques of uniform convergence. The main difficulty we overcome here is to bound the offset. For the consistency with hypothesis space, we present a counterexample. [ABSTRACT FROM AUTHOR]

The article discusses formal control techniques within the field of energy-efficient design. Formal control-theoretic techniques have proved useful in many dynamic control scenarios--from aerospace systems to manufacturing--because their formal underpinnings let designers analyze and bound worst case behavior. In microprocessor power control, such techniques let designers reason about individual control responses and enable them to predict response behavior when individual responses are composed into an overarching complex control response. The goal of DVFS is to scale the frequency to match varying performance demand, or workload changes. Because changing clock frequency will alter the execution speed, a perfect DVFS scheme will lead to a perfect match between the demand and domain execution capability with no performance degradation or energy waste because of a mismatch between the two. The article thus address the formal control-theoretic aspects of such mechanisms, pointing the community toward an era of mathematically provable, robust control algorithms.

This paper proposes a new method for natural-image deblur based on a single blurred image. The natural image prior, a sparse gradient distribution, is enforced using a gradient histogram remapping method in the proposed deblur algorithm. The proposed objective function for blind deconvolution is solved by an alternating minimization method. The point spread function and the unblurred image are updated alternately. The proposed method is able to produce high-quality deblurred results with low computational costs. Both synthetic and real blurred images are tested in the experiments. Encouraging experimental results show that the newly proposed method could effectively restore images blurred by complex motion. [ABSTRACT FROM AUTHOR]

We consider the minimum error entropy (MEE) criterion and an empirical risk minimization learning algorithm when an approximation of Rényi's entropy (of order 2) by Parzen windowing is minimized. This learning algorithm involves a Parzen windowing scaling parameter. We present a learning theory approach for this MEE algorithm in a regression setting when the scaling parameter is large. Consistency and explicit convergence rates are provided in terms of the approximation ability and capacity of the involved hypothesis space. Novel analysis is carried out for the generalization error associated with Rényi's entropy and a Parzen windowing function, to overcome technical difficulties arising from the essential differences between the classical least squares problems and the MEE setting. An involved symmetrized least squares error is introduced and analyzed, which is related to some ranking algorithms. [ABSTRACT FROM AUTHOR]