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The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to semi-supervised learning applied to the least squares classifier. We show that a soft-label and a hard-label variant of self-learning can be derived by applying block coordinate descent to two related but slightly different...
Many research works have successfully extended algorithms such as evolutionary algorithms, reinforcement agents and neural networks using “opposition-based learning” (OBL). Two types of the “opposites” have been defined in the literature, namely type-I and type-II. The former are linear in nature and applicable to the variable space, hence easy to calculate. On the other hand, type-II opposites capture...
This paper considers the long-term network resource allocation problem subject to queue stability. The dynamic problem is first reformulated as a static stochastic programming. To tackle the resultant static programming, we study its dual problem which contains finite number of variables in oppose to the primal problem that has infinite dimension. A novel online framework is developed by formulating...
In this paper, we focus on training a classifier from large-scale data with incompletely assigned labels. In other words, we treat samples with following properties: 1. assigned labels are definitely positive, 2. absent labels are not necessarily negative, and 3. samples are allowed to take more than one label. These properties are frequently found in various kinds of computer vision tasks, including...
Multi-label data with high dimensionality arise frequently in data mining and machine learning. It is not only time consuming but also computationally unreliable when we use high-dimensional data directly. Supervised dimensionality reduction approaches are based on the assumption that there are large amounts of labeled data. It is infeasible to label a large number of training samples in practice...
In the ensemble learning methods for training individual learners in a committee machine, two learning items should be optimized, including minimization of both the squared difference between the target and the learner's output and the estimated correlation between the learner and the rest of learners in the ensemble. The first term is to force each learner to learn the given data. The second term...
Data volume has been increasing explosively in recent years and learning methods are vitally important to extract key information in such mass data. Traditional offline learning requires multiple traversals to the dataset, thus frequently suffering from lack of computational resources. Online learning can benefit in shrinking total time consumed by training model and lowering computational capacity...
Computational analysis of transcription factor binding site (TFBS) is one of the most challenging topics in bioinformatics. A set of TFBS sequences is a type of multiple sequence alignment (MSA). Thus, the hidden Markov model (HMM), as a powerful tool to model MSA, has been extensively applied in TFBS analysis. However, with the sizes of TFBS problems, training HMM in a deterministic way is computationally...
We consider learning problems over training sets in which both, the number of training examples and the dimension of the feature vectors, are large. To solve these problems we propose the random parallel stochastic algorithm (RAPSA). We call the algorithm random parallel because it utilizes multiple processors to operate in a randomly chosen subset of blocks of the feature vector. We call the algorithm...
PROXTONE is a novel and fast method for the optimization of large scale non-smooth convex problems [1]. In this work, we try to use the PROXTONE method in solving large scale non-smooth non-convex problems, for example training of sparse deep neural networks (sparse DNN) or sparse convolutional neural networks (sparse CNN) for embedded or mobile device. PROXTONE converges much faster than first order...
A new technique for the training of ANNs is presented. The time-domain vibration signals of rolling bearings with different fault conditions are preprocessed using differential evolution method, then further being trained by Levenberg Marquardt method. The processed data are applied as input vectors to artificial neural networks (ANNs) for rolling bearing fault classification. The hybrid training...
One of the most investigated methods to increase the accuracy of convolutional neural networks (CNN) is by increasing its depth. However, increasing the depth also increases the number of parameters, which makes convergence of back-propagation very slow and prone to overfitting. Convolutional networks with deep supervision (CNDS) add auxiliary branch to addresses the problem of slower convergence...
Low Rank Matrix Factorization (LRMF) is a classical problem that arises in a wide range of practical contexts, especially in collaborative filtering, dimension reduction, etc. In this paper, a stochastic alternating minimization approach applied to LRMF problem is proposed. The main idea of the approach is to randomly sample partial rows of the matrix to perform parameter update during training using...
Full-Batch update and mini-batch update are two most widely used algorithms in back-propagation(BP) neural network, to deal with the huge training time and computation cost in the learning process. Parallel computing can improve the computation efficiency and have implemented these two algorithms on Mapreduce framework. In this paper, we implement these two algorithms on Spark framework and evaluate...
Deep convolutional neural networks (DCNN's) have shown great value in approaching highly challenging problems in image classification. Based on the successes of DCNNs in scene classification and object detection and localization it is natural to consider whether they would be effective for much simpler computer vision tasks. Our work involves the application of a DCNN to the relatively simple task...
In analysis dictionary learning, the learned dictionary may contain similar atoms, leading to a degenerate dictionary. To address this problem, we propose a novel incoherent analysis dictionary learning algorithm with the ℓ1-norm for sparsity and simultaneously with the coherence penalty. The whole problem is convex but nonsmooth due to the sparsity regularizer and the coherence penalty. Hence, the...
Restricted Boltzmann Machine (RBM) is a generative stochastic energy-based model of artificial neural network for unsupervised learning. Recently, RBM is well known to be a pre-training method of Deep Learning. In addition to visible and hidden neurons, the structure of RBM has a number of parameters such as the weights between neurons and the coefficients for them. Therefore, we may meet some difficulties...
In this paper, we use a combination of support vector machine to improve the Standard SVM, which combine different kernel functions to improve the SVM' learning ability and generalization ability, thereby improving the performance of a combination SVM kernel function, and avoiding the assertiveness of the single prediction model. Combination forecasting model to make joint decisions on the results,...
Both boosting and deep stacking sequentially train their units taking into account the outputs of the previously trained learners. This parallelism suggests that it exists the possibility of getting some advantages by combining these techniques, i.e., emphasis and injection, in appropiate manners. In this paper, we propose a first mode for such a combination by simultaneously applying a general and...
We introduce and analyse a flexible and efficient implementation of Bayesian dictionary learning for sparse coding. By placing Gaussian-inverse-Gamma hierarchical priors on the coefficients, the model can automatically determine the required sparsity level for good reconstructions, whilst also automatically learning the noise level in the data, obviating the need for heuristic methods for choosing...
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