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In today's technology industry where machine learning has become essential, the effectiveness of algorithms ultimately depends on a robust data pipeline, and fast model prototyping and tuning require easy feature discovery and consumption. Careful management of ETL processes and their produced datasets is key to both model development in the research stage and model execution in the production environment...
Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned...
As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training...
Ensemble learning, especially selective ensemble learning is now becoming more and more popular in the field of machine learning. This paper introduces a new ensemble algorithm, named Lasso-Bagging Trees ensemble algorithm. This algorithm is in order to improve the whole learning ability, which is a combination of tree predictors and this method chooses and ensembles trees based on the shrinkage estimation...
A novel edge sampling scheme for IP traceback against DDoS attacks is presented, which is called router's vector edge sampling (RVES). It is simple for marking machines to be implemented. A packet will be probabilistically pre-marked and post-marked on traversed router's interfaces. This approach supports incremental deployment, which makes it effective for multi-path attack reconstruction and computation...
This Internet traffic classification using Machine Learning is an emerging research field since 1990's, and now it is widely used in numerous network activities. The classification technique focuses on modeling attributes and features of data flows to accomplish the identification of applications. In the paper we design and implement the classification model based on header-derived flow statistical...
Although graphics processing unit (GPU) acceleration makes possible interactive volume rendering, successful volume visualization relies on the ability to quickly and correctly classify the volume into different materials or features. Among various classification techniques, one very attractive and effective method is employing machine learning to classify the whole volume according to some minimum...
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