Analyzing sentiment hidden in Sina Weibo's huge amount of information can benefit online marketing, branding, customer relationship management and monitoring public opinions. In this paper, we show how a recursive...
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ISBN:
(纸本)9781479965137
Analyzing sentiment hidden in Sina Weibo's huge amount of information can benefit online marketing, branding, customer relationship management and monitoring public opinions. In this paper, we show how a recursive neural network can be trained to classify Sina Weibo messages' sentiment. Considering syntactic and semantic meaning of the sentence, this method is much superior to just basing on sentiment dictionary. Extensive experiments on huge dataset of Sina Weibo demonstrate that this model consistently outperforms existing sentiment classification model on identifying hidden or implied sentiment.
Mining the large volume textual data produced by microblogging services has attracted much attention in recent years. An important preprocessing step of microblog text mining is to convert natural language texts into ...
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ISBN:
(纸本)9781479975921
Mining the large volume textual data produced by microblogging services has attracted much attention in recent years. An important preprocessing step of microblog text mining is to convert natural language texts into proper numerical representations. Due to the short-length characteristic, finding proper representations of microblog texts is nontrivial. In this paper, we propose to build deep network-based models to learn low-dimensional representations of microblog texts. The proposed models take advantage of the semantic relatedness derived from two types of microblog-specific information, namely the retweet relationship and hashtags. Experiment results show that the deep models perform better than traditional dimensionality reduction methods such as latent semantic analysis and latent Dirichlet allocation topic model, and the use of microblog-specific information can help to learn better representations.
Electric power SCADA (Supervisory Control and Data Acquisition) system gradually transforming from a separate private network to an open public network, seriously increases the vulnerability risk in electric power SCA...
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Electric power SCADA (Supervisory Control and Data Acquisition) system gradually transforming from a separate private network to an open public network, seriously increases the vulnerability risk in electric power SCADA. In order to assess the vulnerability risk in electric power SCADA system, the paper firstly uses Delphi method and AHP (Analytic Hierarchy Process) to build an index system of vulnerability risk assessment, to fully represent the vulnerability of electric power SCADA system. As index data of vulnerability risk assessment in power SCADA is characterized by strong relation and high dimensionality, the method of autoencoder is proposed to reduce dimensionality of index data by representing high-dimensional data in a low dimensional space. Auto encoder method can obtain the optimal initial weight in pre-training and then back-propagate error derivatives adjusting weights with the initial weights to minimize the reconstruction error finally getting the best reconstructed results. The paper conducts simulation experiments about reconstruction error in pre-training and fine-tuning process in MATLAB experimental platform, and the experimental results show that dimensional code received by reducing dimensionality of data can basically fully represent high-dimensional data. The lowdimensional code as input can significantly reduce the complexity in the construction of model of vulnerability risk assessment in Electric power SCADA system in later work.
The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory facto...
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The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.
Precise determination of load profiles is a key process in optimal control of power distribution systems. The emerging need for electricity, the penetration of distributed local generation and the rising power quality...
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ISBN:
(纸本)9781479914647
Precise determination of load profiles is a key process in optimal control of power distribution systems. The emerging need for electricity, the penetration of distributed local generation and the rising power quality requirements imposes more advanced algorithms to be used. In this paper locality sensitive hashing is presented, which uses feature sets extracted from load data by autoencoders.
We present an efficient online learning scheme for non-negative sparse coding in autoencoder neural networks. It comprises a novel synaptic decay rule that ensures non-negative weights in combination with an intrinsic...
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We present an efficient online learning scheme for non-negative sparse coding in autoencoder neural networks. It comprises a novel synaptic decay rule that ensures non-negative weights in combination with an intrinsic self-adaptation rule that optimizes sparseness of the non-negative encoding. We show that non-negativity constrains the space of solutions such that overfitting is prevented and very similar encodings are found irrespective of the network initialization and size. We benchmark the novel method on real-world datasets of handwritten digits and faces. The autoencoder yields higher sparseness and lower reconstruction errors than related offline algorithms based on matrix factorization. It generalizes to new inputs both accurately and without costly computations, which is fundamentally different from the classical matrix factorization approaches. (C) 2012 Elsevier Ltd. All rights reserved.
The majority of distribution management functionalities rely on load profiles. Customer classification and load analysis have the largest impact on them. In this paper a novel approach for load profile generation is p...
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ISBN:
(纸本)9781467359405
The majority of distribution management functionalities rely on load profiles. Customer classification and load analysis have the largest impact on them. In this paper a novel approach for load profile generation is presented. The presented work is based on artificial neural networks: sparse autoencoders and deep belief networks in order to reveal hidden features from data sets.
Nowadays, due to the development of network technology, the Internet becomes the main resource for people to obtain information. The openness of the network makes the network abound of all kinds of information, so it ...
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ISBN:
(纸本)9781632660015
Nowadays, due to the development of network technology, the Internet becomes the main resource for people to obtain information. The openness of the network makes the network abound of all kinds of information, so it becomes more and more important that using network text classification techniques enable people to get the information they are interested in quickly from the mixed and disorderly network information. Since network text classification technology is the basis of information filtering, search engines, and other fields, it has gradually become a research focus. The traditional text classification technology can't effectively support the Chinese web page text classification because of the large scale of Chinese web page text. An important way to learn the data feature from massive data is to use deep learning neural network structure. Deep learning network has excellent feature learning ability. It can combine objects of low-level features to form advanced abstract representations of the object which will be more suitable for classification. This paper proposes a new deep learning based text classification model to solve the problem of Chinese web text categorization of dimension reduction. Moreover we verify the feasibility of this method through the experiment.
This paper presents the results obtained for medical image compression using autoencoder neural networks. Since mammograms (medical images) are usually of big sizes, training of autoencoders becomes extremely tedious ...
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This paper presents the results obtained for medical image compression using autoencoder neural networks. Since mammograms (medical images) are usually of big sizes, training of autoencoders becomes extremely tedious and difficult if the whole image is used for training. We show in this paper that the autoencoders can be trained successfully by using image patches instead of the whole image. The compression performances of different types of autoencoders are compared based on two parameters, namely mean square error and structural similarity index. It is found from the experimental results that the autoencoder which does not use Restricted Boltzmann Machine pre-training yields better results than those which use this pre-training method.
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