Finally, we identify certain primitive operators that are useful for a large class of data mining and decision support applications. Supporting them natively in the DBMS could enable these applications to run faster.University of Florida.Thomas, Shiby....
The research and development issues cover a wide range of fields, many of which are shared with media processing, signal processing, database technologies, and data mining. 展开 关键词:multimedia information indexing retrieval architecture system ...
Deep Learning(DL)is a subfield of machine learning that significantly impacts extracting new knowledge.By using DL,the extraction of advanced data representations and knowledge can be made possible.Highly effective DL techniques help to find more hidden knowledge.Deep learning has a promising future du...
The proliferation of embedded systems, wireless technologies, and Internet protocols have enabled the IoT to bridge the gap between the virtual and physical world enabling the monitoring and control of the environment by data processing systems. IoT refers to the inter-networking of everyday objects ...
Your app uses Core ML APIs and user data to make predictions, and to train or fine-tune models, all on the user's device. A model is the result of applying a machine learning algorithm to a set of training data. You use a model to make predictions based on new input data. ...
Emerging Big Data applications require a significant amount of server computational power. Big data analytics applications rely heavily on specific deep machine learning and data mining algorithms, and exhibit high computational intensity, memory intensity, I/O intensity and control intensity. Big data ap...
This paper elaborates on the justification and need for integration of OLTP (online transaction processing) and OLAP (online analytical processing) data en... SS Conn - Southeastcon, IEEE 被引量: 79发表: 2005年 Coarse Grained Parallel On-Line Analytical Processing (OLAP) for Data Mining Summary...
Recurrent neural networks (RNNs) are neural network architectures with hidden state and which use feedback loops to process a sequence of data that ultimately informs the final output. Therefore, RNN models can recognize sequential characteristics in the data and help to predict the next likely dat...
a neural NLP model such as a recurrent neural network (RNN) learns an extremely wide variety of SMILES from public databases11,12,13, converts the string into a low-dimensional vector, decodes it back to the original SMILES, and then the intermediate vector is drawn out as a descriptor....
Cloud-based Big Data andMachine Learning(ML) applications[1],[2]are becoming increasingly popular in the industry, also in academic and education sectors. In many cases, clouds are used to support the computation and storage needs of such applications by building and managing multi-VM virtual in...