Results 21 to 30 of about 8,510,788 (298)
Effective self-training for parsing [PDF]
Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics -, 2006 We present a simple, but surprisingly effective, method of self-training a two-phase parser-reranker system using readily available unlabeled data. We show that this type of bootstrapping is possible for parsing when the bootstrapped parses are processed by a discriminative reranker.
David McClosky +2 more
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Deep Bayesian Self-Training [PDF]
Neural Computing and Applications, 2019 Supervised Deep Learning has been highly successful in recent years, achieving state-of-the-art results in most tasks. However, with the ongoing uptake of such methods in industrial applications, the requirement for large amounts of annotated data is often a challenge.
Fabio De Sousa Ribeiro +5 more
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IEEE Access, 2023 Positive and unlabelled (PU) learning for multi-variate time series classification refers to build a binary classification model when only a small set of positive and a large set of unlabelled samples are accessible at training stage.
Dino Ienco
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Self-Training with Selection-by-Rejection [PDF]
2012 IEEE 12th International Conference on Data Mining, 2012 Practical machine learning and data mining problems often face shortage of labeled training data. Self-training algorithms are among the earliest attempts of using unlabeled data to enhance learning. Traditional self-training algorithms label unlabeled data on which classifiers trained on limited training data have the highest confidence. In this paper,
Yan Zhou 0001 +2 more
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Journal of Ultrasonography, 2017 Aim: The goal of the work was comparing gas ultrasound images below the right diaphragm in two groups: in people with intestinal interposition below the diaphragm and ones with pneumoperitoneum and extracting the traits differentiating these two ...
Andrzej Smereczyński +1 more
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Semi-Supervised Self-Training Feature Weighted Clustering Decision Tree and Random Forest
IEEE Access, 2020 A self-training algorithm is an iterative method for semi-supervised learning, which wraps around a base learner. It uses its own predictions to assign labels to unlabeled data.
Zhenyu Liu +3 more
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Annual report (Online); Other title: Title from file name: OPOTA annual report
Annual; Report for 2008 covers the calendar year; report for 2009 not published; report for 2010- covers the state fiscal year (July-June)
Ohio Peace Officer Training Commission.
core +6 more sources
Treatment of Malignant Glaucoma
Beyoglu Eye Journal, 2017 Malignant glaucoma is one of the most challenging conditions in ophthalmology. The exact etiology of this complication is not fully understood, and its management is usually challenging.
Cigdem Altan +2 more
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Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform
Applied Sciences, 2020 Traffic monitoring from closed-circuit television (CCTV) cameras on embedded systems is the subject of the performed experiments. Solving this problem encounters difficulties related to the hardware limitations, and possible camera placement in various ...
Sebastian Cygert, Andrzej Czyżewski
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Remote Sensing, 2023 Semantic segmentation based on optical images can provide comprehensive scene information for intelligent vehicle systems, thus aiding in scene perception and decision making.
Ziquan Wang +6 more
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