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Semisupervised scheme has emerged as a popular strategy in the machine learning community due to the expensiveness of getting enough labeled data. In this paper, a semisupervised incremental support vector machine (SE-INC-SVM) algorithm based on neighborhood kernel estimation is proposed. First, kernel regression is constructed to estimate the unlabeled data from the labeled neighbors and its estimation...
Existing intelligent theoretical line losses calculation methods that prevalent on worse line calculation error, are all based on single learning algorithm. In order to overcome this defect, a novel intelligent calculation method based on boosting algorithm is proposed. In this calculation method, the theoretical line losses calculation is abstracted into function fitting problem, in addition, the...
Recommender system has become one of the most promising techniques in the era of big data. It aims to help users to quickly find the valuable information from the massive data. Many recommendation approaches have been proposed in recent years. Currently, a majority of researchers still pay attention on designing more effective and efficient methods, and they usually put all the user data into model...
In time series forecasting, the artificial neural networks (NN) such as the popular multilayer perceptron (MLP) may be used to handle both linearity and nonlinearity underlying the data generating process, but finding a right network size such as the number of hidden layers and/or hidden units is always a troublesome and time-consuming task. This paper presents a time series prediction model that...
The goal of this article is to present an effective and robust tracking algorithm for nonlinear feet motion by deploying particle filter integrated with Gaussian process latent variable model and embedded with Markov-switching approach. Training trajectory data is projected from the observation space to the latent space of lower dimensionality in a nonlinear probabilistic manner. In the latent space,...
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