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Neighbors embedding is a promising method for single image super-resolution (SR). However, the fixed number of neighbors for different kind of input low resolution (LR) patches may be improper. In addition, the assumption that low resolution space and high resolution (HR) space has similar local geometry leads to improper HR patches are used for reconstruction. In this paper, we propose a novel single...
Matrix factorization (MF) is a major technique for collaborative filtering of recommender systems. However, in the traditional MF model, it is difficult to tune the regularization parameter, and the predicted ratings may not lie within the given range. In this paper, we propose a new MF approach, in which MF is modeled as a constrained optimization problem and the constraint conditions are given in...
Restricted Boltzmann Machine (RBM) has been successfully applied to many different machine learning and pattern recognition problems. Usually, fixed learning rate (FLR) is used for training RBM. However, the reconstruction error (RCERR) with FLR may not be declined each iteration, which will result in a slow convergence speed. In this paper, we propose a method to dynamically choose the learning rate...
Medical Informatics is the scientific field that deals with the storage, retrieval and optimal use of information and data in medicine. It is often called healthcare informatics or biomedicai informatics, and forms part of the wider domain of eHealth. The end objective of biomedicai informatics is the coalescing of data, knowledge, and the tools necessary to apply that data and knowledge in the decision-making...
This paper proposes a new RNN - shared reservoir modular echo-state networks (SRMESNs), which has a higher forecast precision when the amount of training data is large enough. First, the neural state space is divided into several subspaces. And then the data belonging to each subspace is put into the same reservoir. But for each subspace, we set up an independent output weight vector respectively...
In many pattern recognition and data mining tasks, we often confront the problem of learning from a large amount of unlabeled data only with few pairwise constraints. This learning style is a kind of semi-supervised learning, and these pairwise constraints are called Side-Information. Generally speaking, these pairwise constraints are divided into two categories, one is called must-link if the pair...
Considering that each sentence element of a sentence or clause plays an important role in describing case and (or) object in documents, a feature extraction method based on sentence element is proposed in this paper. The method can extract feature terms from documents effectively and weight them accurately. It first extracts sentence elements from dependency relationships, and then selects and weights...
Pseudo random bit generator is widely used in BIST for test pattern generation. Typical pseudo random bit generator adopts linear feedback shift register (LFSR) as its basic circuit. Dynamic LFSR (DLFSR[1]) which has better cryptographic properties with respect to typical LFSR consumes more power. This paper forwards a low power DLFSR (LDLFSR) circuit which achieves comparable performance with less...
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