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Inverse halftoning by decision tree learning [PDF]

Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2004
Inverse halftoning is the process to retrieve a (gray) continuous-tone image from a halftone. Recently, machine-learning-based inverse halftoning techniques have been proposed. Decision-tree learning has been applied with success to various machine-learning applications for quite some time.
R.L. de Queiroz, Hae Yong Kim
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Decision Tree Learning

2016
The main objective of this chapter is to introduce you to hierarchical supervised learning models. One of the main hierarchical models is the decision tree. It has two categories: classification tree and regression tree. The theory and applications of these decision trees are explained in this chapter.
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Learning probabilistic decision trees for AUC

Pattern Recognition Letters, 2006
Accurate ranking, measured by AUC (the area under the ROC curve), is crucial in many real-world applications. Most traditional learning algorithms, however, aim only at high classification accuracy. It has been observed that traditional decision trees produce good classification accuracy but poor probability estimates.
Jiang Su, Harry Zhang
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Selecting learning activities with decision trees

Proceedings of the 2012 IEEE Global Engineering Education Conference (EDUCON), 2012
Technical developments and a more and more flexible relation between working and private life lead to the inclusion of both formal and informal aspects in learning concepts, as well as supported and unsupported phases, with a flexible role of learners, tutors, and teachers.
Christian Schonfeldt, Anja Thomanek, Djamshid Tavangarian, Mario Donick   +3 more
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Decision tree-based paraconsistent learning

Proceedings. SCCC'99 XIX International Conference of the Chilean Computer Science Society, 2003
It is possible to apply machine learning, uncertainty management and paraconsistent logic concepts to the design of a paraconsistent learning system, able to extract useful knowledge even in the presence of inconsistent information in a database. This paper presents a decision tree-based machine learning technique capable of handling inconsistent ...
Robert Sabourin, Bráulio Coelho Ávila, Fabrício Enembreck   +2 more
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Exemplar learning in fuzzy decision trees [PDF]

Proceedings of IEEE 5th International Fuzzy Systems, 2002
Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data.
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Streaming Decision Trees for Lifelong Learning

2021
Lifelong learning models should be able to efficiently aggregate knowledge over a long-term time horizon. Comprehensive studies focused on incremental neural networks have shown that these models tend to struggle with remembering previously learned patterns.
Bartosz Krawczyk, Łukasz Korycki
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Decision Trees Learning System

2002
This paper describes computer system — Decision Trees Learning System (DTLS) that was developed as a main part of the Master Thesis: “Implementation of the algorithms of learning decision trees, working on any SQL compatible database”. Developed System is friendly, credible environment for searching and modeling knowledge stored in databases, using ...
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Transfer Learning on Decision Tree with Class Imbalance

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019
Transfer learning has attracted growing attention from the machine learning community. It addresses real-world issues that were not considered in the classical methods, especially with model-based methods when source data is not available. Recent work on transfer learning for Decision Trees has been proposed in the form of two algorithms named SER and ...
Minvielle, Ludovic, Atiq, Mounir, Peignier, Sergio, Mougeot, Mathilde   +3 more
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Decision tree learning with fuzzy labels

Information Sciences, 2005
Label semantics is a random set based framework for ''Computing with Words'' that captures the idea of computation on linguistic terms rather than numerical quantities.
Qin, Z, Lawry, J
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