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Improved bio-inspired with machine learning computing approach for thyroid prediction. [PDF]

Sci Rep
Kesavulu D, R K.
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Learning fuzzy decision trees

Neural Networks, 1998
We present a recurrent neural network which learns to suggest the next move during the descent along the branches of a decision tree. More precisely, given a decision instance represented by a node in the decision tree, the network provides the degree of membership of each possible move to the fuzzy set z.Lt;good movez.Gt;.
B. APOLLONI, G. ZAMPONI, A. M. ZANABONI
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Learning decision tree for ranking

Knowledge and Information Systems, 2008
Decision tree is one of the most effective and widely used methods for classification. However, many real-world applications require instances to be ranked by the probability of class membership. The area under the receiver operating characteristics curve, simply AUC, has been recently used as a measure for ranking performance of learning algorithms ...
Liangxiao Jiang, Zhihua Cai, Chaoqun Li
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Transfer Learning in Decision Trees

2007 International Joint Conference on Neural Networks, 2007
Most research in machine learning focuses on scenarios in which a learner faces a single learning task, independently of other learning tasks or prior knowledge. In reality, however, learning is not performed in isolation, starting from scratch with every new task. Instead, it is a lifelong activity during which a learner encounters many learning tasks,
Jun Won Lee, Christophe Giraud-Carrier
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Decision tree learning with fuzzy labels

Information Sciences, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Qin, Z, Lawry, J
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Learning Monotone Decision Trees in Polynomial Time

SIAM Journal on Computing, 2006
We give an algorithm that learns any monotone Boolean function f : {−1, 1}n → {−1, 1} to any constant accuracy, under the uniform distribution, in time polynomial in n and in the decision tree size of f. This is the first algorithm that can learn arbitrary monotone Boolean functions to high accuracy, using random examples only, in time polynomial in a ...
Ryan O'Donnell, Rocco A. Servedio
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Competitive learning in decision trees

AIP Conference Proceedings, 1998
In this paper, a competitive learning rule is introduced in decision trees as a computationally attractive scheme for adaptive density estimation or lossy compression. It is shown by simulation that the adaptive decision tree performs at least as well as other competitive learning algorithms while being much faster.
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