This paper introduces AbstractNet, a generative model for high density inputs. The model suggests a method that uses unsupervised learning to generate feature maps. The model drastically improves the performances of r...
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ISBN:
(纸本)9783319729268;9783319729251
This paper introduces AbstractNet, a generative model for high density inputs. The model suggests a method that uses unsupervised learning to generate feature maps. The model drastically improves the performances of raw audio generation by reducing the required amount of input data and computing power necessary to achieve a similar result when compared to the state of the art.
Recently, with the rapid development of big data, the accuracy, speed, efficiency and the cost of remote sensing yield estimation have been greatly improved. Based on the relevant literature researches of domestic and...
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ISBN:
(纸本)9781538643990
Recently, with the rapid development of big data, the accuracy, speed, efficiency and the cost of remote sensing yield estimation have been greatly improved. Based on the relevant literature researches of domestic and foreign scholars in the remote sensing yield estimation for nearly ten years, we divide the process into four stages: the crop planting area extraction, the crop growth monitoring, the crop yield estimation model, and the model accuracy evaluation. In this paper, we also conclude the characteristics of each stage, evaluate the advantages and disadvantages of the commonly used methods in each stage, and explore solutions to some main problems in the field of remote sensing yield estimation, finally, through analysis and summary, we give some feasible suggestions to use the models in different situations to provide references for the future research.
Deep convolution neural networks (CNN) have demonstrated remarkable performance on visual recognition tasks. This performance is feasible due to availability of massive datasets used for training of the networks, and ...
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Nowadays, the researches in data mining area have been continuous increasing. Appling data mining to agriculture;for example, the prediction of rice produce for farmers is still challenging. The objective of the resea...
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ISBN:
(纸本)9781538649916
Nowadays, the researches in data mining area have been continuous increasing. Appling data mining to agriculture;for example, the prediction of rice produce for farmers is still challenging. The objective of the research is to propose a model using Machine Learning Techniques comparing between Decision Tree Technique and Neural Network Technique (ANN) for the prediction of rice produce for farmers. Farmers can predict volume of rice produce and selling price. It is helpful for farmers to increase their income. The process of the research follows Cross-industry standard process for data mining (CRISP-DM) process. The model pattern is classified by machine learning techniques experiment with a dataset of farmer records. Performance measure of model pattern uses four options such as Test Options, Cross-Validation Folds 10, Split 80-20, and Use Training Set. After that, four options will be averaged for accuracy. The experimental result shows that the best technique which has highest accuracy can be helpful for farmers in real world.
With the increment of data scale,distributed machine learning has received more and more ***,as the data grows,the dimension of the dataset will increase rapidly,which leads to the increment of the communication traff...
With the increment of data scale,distributed machine learning has received more and more ***,as the data grows,the dimension of the dataset will increase rapidly,which leads to the increment of the communication traffic in the distributed computing cluster and decreases the performance of the distributed *** paper proposes a message filtering strategy based on asynchronous alternating direction method of multipliers（ADMM）,which can effectively reduce the communication time of the algorithm while ensuring the convergence of the *** this paper,a soft threshold filtering strategy based on L1 regularization is proposed to filter the parameter of master node,and a gradient truncation filtering strategy is proposed to filter the parameter of slave ***,we update the algorithm asynchronously to reduce the waiting time of the master *** on largescale sparse data show that our algorithm can effectively reduce the traffic of messages and make the algorithm reach convergence in a shorter time.
With the increasing popularity of online shopping, it has brought with its massive online consumers and the growth of merchandise information data. In order to deal with the demand for big data processing, building an...
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ISBN:
(纸本)9783319745213;9783319745206
With the increasing popularity of online shopping, it has brought with its massive online consumers and the growth of merchandise information data. In order to deal with the demand for big data processing, building an analysis system of e-commerce reviews base on Hadoop software framework. The reviews of Internet commodity are chosen to be the samples of study. Choosing Navie Bayesian classification to analyze the attributed values are discrete. The classification algorithms in accordance with MapReduce parallel computing theory designed and run on Hadoop platform. Constructing the Naive Bayesian sentiment classifier, and make the classifiers on the Hadoop platform to achieve commodity reviews mining job. Result shows that it can improve the efficiency of the commodity reviews analysis by using the Hadoop distributed platform.
A density peaks clustering based on improved RNA genetic algorithm (DPC-RNAGA) is proposed in this paper. To overcome the problems of Clustering by fast search and find of density peaks (referred to as DPC), DPC-RNAGA...
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ISBN:
(数字)9783319745213
ISBN:
(纸本)9783319745213;9783319745206
A density peaks clustering based on improved RNA genetic algorithm (DPC-RNAGA) is proposed in this paper. To overcome the problems of Clustering by fast search and find of density peaks (referred to as DPC), DPC-RNAGA uses exponential method to calculate the local density, In addition, improved RNA-GA was used to search the optimums of local density and distance. So clustering centers can be determined easily. Numerical experiments on synthetic and real-world datasets show that, DPC-RNAGA can achieve better or comparable performance on the benchmark of clustering, adjusted rand index (ARI), compared with K-means, DPC and Max_Min SD methods.
data matching and retrieval aims at finding out similar substrings with the pattern P in the given data set T. This problem has wide applications in big data analysis. A liberalized verification rule is proposed first...
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ISBN:
(数字)9783319745213
ISBN:
(纸本)9783319745213;9783319745206
data matching and retrieval aims at finding out similar substrings with the pattern P in the given data set T. This problem has wide applications in big data analysis. A liberalized verification rule is proposed first, and then a similarity computing based order preserving matching method is presented. Theory analysis indicates our method runs in linear. Furthermore, the experimental results show that our method can improve effectively the precision ratio and the recall ratio. More qualified matching results can be detected compared with the state of the art of this problem.
Based on time-series detection algorithm, this paper puts forward a new analysis method for identify Network Element (NE) hitches. Aiming at specific characteristics of the NE, this paper propose a model which conside...
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ISBN:
(纸本)9783319745213;9783319745206
Based on time-series detection algorithm, this paper puts forward a new analysis method for identify Network Element (NE) hitches. Aiming at specific characteristics of the NE, this paper propose a model which consider seasonal timing characteristics and impact of current data from recent data. Considering of multi-dimensional characteristics of NE, a density-based discovery algorithm is introduced into the modeling process. Experiments on the actual data coming from operates demonstrate the effectiveness and accuracy of the proposed methods.
The proceedings contain 10 papers. The special focus in this conference is on Algorithmic Aspects of Cloud computing. The topics include: A walk in the clouds: Routing through vnfs on bidirected networks;Service chain...
ISBN:
(纸本)9783319748740
The proceedings contain 10 papers. The special focus in this conference is on Algorithmic Aspects of Cloud computing. The topics include: A walk in the clouds: Routing through vnfs on bidirected networks;Service chain placement in SDNs;tight approximability of the server allocation problem for real-time applications;risk aware stochastic placement of cloud services: The case of two data centers;towards an algebraic cost model for graph operators;computing probabilistic queries in the presence of uncertainty via probabilistic automata;improving rule-based elasticity control by adapting the sensitivity of the auto-scaling decision timeframe;risk aware stochastic placement of cloud services: The multiple data center case.
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