Kuaa: A unified framework for design, deployment, execution, and recommendation of machine learning experiments
TL;DR: This work proposes the use of similarity measures and learning-to-rank methods (LRAR) in the implementation of the recommendation service, and shows that Jaro–Winkler yields the highest effectiveness performance with comparable results to those observed for LRAR.
About: This article is published in Future Generation Computer Systems.The article was published on 2018-01-01. It has received 2 citations till now. The article focuses on the topics: Active learning (machine learning) & Online machine learning.
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TL;DR: A novel recommender system in the multi-cloud with the use of proposed machine learning algorithm (NPCA-HAC) where the social data set are pre-processed to remove the noise and making them pure and the ranked output was evaluated and the performance measure was analyzed which provides the efficient results.
Abstract: A recommender system or a recommendation system is a subclass of information filtering system which in turn predicts the “preference” or “ratings” which a user would provide to the specified item. Recommender systems are utilized in a variety of areas comprising news, music, movies, books, search queries, social tags, research articles, and products in general. The primary aim of the recommender system is to allow the computers learn automatically without any human intervention or assistance and regulate activities consequently. The existing methods had a lower amount of search result quality and a minimum rate of ranking accuracy. To overcome this issue and to enhance the ranking quality and search result quality a novel recommender system in the multi-cloud with the use of proposed machine learning algorithm. In this proposed work (NPCA-HAC), the social data set are pre-processed to remove the noise and making them pure. Then, the method of feature selection is carried out with the use of principle component analysis method (PCA). The selected features are then clustered with the use of k-means followed by the Hierarchical Agglomerative Clustering algorithm (HAC). These clusters are then ranked by the use of trust ranking algorithm. Finally, the ranked output was evaluated and the performance measure was analyzed which provides the efficient results from the recommender system.
14 citations
TL;DR: In this article, the authors provide an alternative solution that devises a MLOps platform with open source frameworks on any virtual resources and demonstrate a working example of training and deploying a machine learning model for detecting software repository code vulnerability.
8 citations
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Journal Article•
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.
47,974 citations
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
40,826 citations
TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.
Abstract: CONSIDER Table 1. It represents in its formal characteristics a situation which arises in the clinical-social-personality areas of psychology, where it frequently occurs that the only useful level of measurement obtainable is nominal scaling (Stevens, 1951, pp. 2526), i.e. placement in a set of k unordered categories. Because the categorizing of the units is a consequence of some complex judgment process performed by a &dquo;two-legged meter&dquo; (Stevens, 1958), it becomes important to determine the extent to which these judgments are reproducible, i.e., reliable. The procedure which suggests itself is that of having two (or more) judges independently categorize a sample of units and determine the degree, significance, and
34,965 citations
20 Jun 2005
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Abstract: We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
31,952 citations
Posted Content•
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from this http URL.
28,898 citations