The Experts below are selected from a list of 46371 Experts worldwide ranked by ideXlab platform
Qing Ma - One of the best experts on this subject based on the ideXlab platform.
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Order Estimation of Japanese Paragraphs by Supervised Machine Learning and Various Textual Features
Journal of Artificial Intelligence and Soft Computing Research, 2015Co-Authors: Masaki Murata, Masato Tokuhisa, Satoshi Ito, Qing MaAbstract:Abstract In this paper, we propose a method to estimate the order of paragraphs by Supervised Machine Learning. We use a support vector Machine (SVM) for Supervised Machine Learning. The estimation of paragraph order is useful for sentence generation and sentence correction. The proposed method obtained a high accuracy (0.84) in the order estimation experiments of the first two paragraphs of an article. In addition, it obtained a higher accuracy than the baseline method in the experiments using two paragraphs of an article. We performed feature analysis and we found that adnominals, conjunctions, and dates were effective for the order estimation of the first two paragraphs, and the ratio of new words and the similarity between the preceding paragraphs and an estimated paragraph were effective for the order estimation of all pairs of paragraphs.
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SCIS&ISIS - Order estimation of Japanese paragraphs by Supervised Machine Learning
2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Syst, 2014Co-Authors: Satoshi Ito, Masato Tokuhisa, Masaki Murata, Qing MaAbstract:In this paper, we propose a method to estimate the order of paragraphs by Supervised Machine Learning. We use a support vector Machine (SVM) for Supervised Machine Learning. The estimation of paragraph order is useful for sentence generation and sentence correction. The proposed method obtained a high accuracy (0.86) in the order estimation experiments of the first two paragraphs of an article and achieved the same accuracy as manual estimation. In addition, it obtained a higher accuracy than the baseline methods in the experiments using two paragraphs of an article. We performed feature analysis and we found that adnominals, conjunctions, and dates were effective for the order estimation of the first two paragraphs, and the ratio of new words and the similarity between the preceding paragraphs and an estimated paragraph were effective for the order estimation of all pairs of paragraphs. Moreover, we compared the order estimation of sentences and paragraphs and clarified differences. For the order estimation of the first two paragraphs, paragraph order estimation would be easier than sentence order estimation because paragraphs have more information than sentences. For the order estimation of all pairs of paragraphs, paragraph order estimation would be more difficult than sentence order estimation because a story may conclude in a paragraph.
Dezso Sera - One of the best experts on this subject based on the ideXlab platform.
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Conditional monitoring in photovoltaic systems by semi-Supervised Machine Learning
Energies, 2020Co-Authors: Lars Maaløe, Ole Winther, Sergiu Spataru, Dezso SeraAbstract:With the rapid increase in photovoltaic energy production, there is a need for smart condition monitoring systems ensuring maximum throughput. Complex methods such as drone inspections are costly and labor intensive; hence, condition monitoring by utilizing sensor data is attractive. In order to recognize meaningful patterns from the sensor data, there is a need for expressive Machine Learning models. However, Supervised Machine Learning, e.g., regression models, suffer from the cumbersome process of annotating data. By utilizing a recent state-of-the-art semi-Supervised Machine Learning based on probabilistic modeling, we were able to perform condition monitoring in a photovoltaic system with high accuracy and only a small fraction of annotated data. The modeling approach utilizes all the unSupervised data by jointly Learning a low-dimensional feature representation and a classification model in an end-to-end fashion. By analysis of the feature representation, new internal condition monitoring states can be detected, proving a practical way of updating the model for better monitoring. We present (i) an analysis that compares the proposed model to corresponding purely Supervised approaches, (ii) a study on the semi-Supervised capabilities of the model, and (iii) an experiment in which we simulated a real-life condition monitoring system.
Walid G. Morsi - One of the best experts on this subject based on the ideXlab platform.
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Non-Intrusive Load Monitoring Using Semi-Supervised Machine Learning and Wavelet Design
IEEE Transactions on Smart Grid, 2017Co-Authors: Jacqueline M Gillis, Walid G. MorsiAbstract:This paper presents a new approach based on semi-Supervised Machine Learning and wavelet design applied to non-intrusive load monitoring. Co-training of two Machine Learning classifiers is used to automate the process of Learning the load pattern after designing new wavelets. The numerical results demonstrating the effectiveness of the proposed approach are discussed and conclusions are drawn.
Lauren Terhorst - One of the best experts on this subject based on the ideXlab platform.
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Iterative processes: a review of semi-Supervised Machine Learning in rehabilitation science.
Disability and rehabilitation. Assistive technology, 2019Co-Authors: Emily A Kringle, Evan C. Knutson, Collin J. Engstrom, Lauren TerhorstAbstract:Purpose: To define semi-Supervised Machine Learning (SSML) and explore current and potential applications of this analytic strategy in rehabilitation research.Method: We conducted a scoping review ...
Patrick Gallinari - One of the best experts on this subject based on the ideXlab platform.
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INEX - Supervised and Semi-Supervised Machine Learning Ranking
Comparative Evaluation of XML Information Retrieval Systems, 2006Co-Authors: Jean-noël Vittaut, Patrick GallinariAbstract:We present a Semi-Supervised Machine Learning based ranking model which can automatically learn its parameters using a training set of a few labeled and unlabeled examples composed of queries and relevance judgments on a subset of the document elements. Our model improves the performance of a baseline Information Retrieval system by optimizing a ranking loss criterion and combining scores computed from doxels and from their local structural context. We analyze the performance of our Supervised and semi-Supervised algorithms on CO-Focussed and CO-Thourough tasks using a baseline model which is an adaptation of Okapi to Structured Information Retrieval.
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Supervised and Semi-Supervised Machine Learning Ranking
2006Co-Authors: Jean-noël Vittaut, Patrick GallinariAbstract:We present a Semi-Supervised Machine Learning based ranking model which can automatically learn its parameters using a training set of a few labeled and unlabeled examples composed of queries and relevance judgments on a subset of the document elements. Our model improves the performance of a baseline Information Retrieval system by optimizing a ranking loss criterion and combining scores computed from doxels and from their local structural context. We analyze the performance of our Supervised and semi-Supervised algorithms on CO-Focussed and CO-Thourough tasks using a baseline model which is an adaptation of Okapi to Structured Information Retrieval.
