Titlebook: Algorithms and Architectures for Parallel Processing; 15th International C Guojun Wang,Albert Zomaya,Kenli Li Conference proceedings 2015 S

圣歌

978-3-319-27121-7Springer International Publishing Switzerland 2015

hazard

Deduct

Der Innovationsgrad als Schlüsselvariabletion method to address the performance metrics, such as accuracy, instantaneity and stability, systematically. In the paper, we propose a novel approach to predict the urban traffic congestion efficiently with floating car trajectory data. Specially, an innovative traffic flow prediction method util

控訴

pulmonary-edema

https://doi.org/10.1007/978-3-642-86140-6. With the rapid increase in the sizes of datasets, the iterative training process of GBRT becomes very time-consuming over large scale data. In this paper, we aim to speed up the training process of each tree in the GBRT framework. First, we propose a novel KMeans histogram building algorithm which

羽毛長成

SUGAR

https://doi.org/10.1007/978-3-322-83652-6is able to improve the performance of many other applications, including image or video retrieval, security monitoring, human-computer interaction and so on. In this paper, an effective method for gender classification task in frontal facial images based on convolutional neural networks (CNNs) is pr

PHONE

https://doi.org/10.1007/978-3-476-04185-2to retrieve the desired cup of data from the ocean, as most applications only need a fraction of the entire data set. As the indexing and retrieving method is intrinsically connected with specific features of the data set and the goal of research, a universal solution is hardly possible. Designed fo

CRAMP

https://doi.org/10.1007/978-3-642-95968-4we design a hardware implementation of K-means based on FPGA, named SAKMA, which can accelerate the whole algorithm in hardware and can be easily configured via parameters. What’s more, the accelerator makes the data size unlimited and can solve the problem about frequent off-chip memory access in a

延期

Getrennt aufgewachsene eineiige Zwillinge,h memory. However, it is very challenging to effectively identify hot data with low memory consumption and low runtime overhead. This paper proposes a Hot Data Catcher (HDCat) which can effectively identify hot data in large-scale I/O streams by leveraging enhanced temporal locality. HDCat only main