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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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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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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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Decision tree-based paraconsistent learning
Proceedings. SCCC'99 XIX International Conference of the Chilean Computer Science Society, 2003It 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 +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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Learning probabilistic decision trees for AUC
Pattern Recognition Letters, 2006Accurate 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), 2012Technical 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 +3 more
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Streaming Decision Trees for Lifelong Learning
2021Lifelong 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
2002This 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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On Lookahead Heuristics in Decision Tree Learning
2003In decision tree learning attribute selection is usually based on greedy local splitting criterion. More extensive search quickly leads to intolerable time consumption. Moreover, it has been observed that lookahead cannot benefit prediction accuracy as much as one would hope.
Tapio Elomaa, Tuomo Malinen
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On Handling Tree-Structured Attributes in Decision Tree Learning
1995Abstract This paper studies the problem of learning decision trees when the attributes of the domain are tree-structured. We first describe two pre-processing approaches, the Quinlan- encoding and the bit-per-category methods, that re-encode the training examples in terms of new nominal attributes.
Hussein Almuallim +2 more
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