Results 151 to 160 of about 526,096 (172)

Optimizing machine learning models for predicting anemia among under-five children in Ethiopia: insights from Ethiopian demographic and health survey data. [PDF]

BMC Pediatr
Yimer A, Yesuf HA, Ahmed S, Zemariam AB, Mussa E, Sirage N, Yesuf A, Kassaw AK.   +7 more
europepmc   +1 more source

Exploring PM_2.5 and PM₁₀ ML forecasting models: a comparative study in the UAE. [PDF]

Sci Rep
Abuouelezz W, Ali N, Aung Z, Altunaiji A, Shah SB, Gliddon D.   +5 more
europepmc   +1 more source

Explainable AI for enhanced accuracy in malaria diagnosis using ensemble machine learning models. [PDF]

BMC Med Inform Decis Mak
Awe OO, Mwangi PN, Goudoungou SK, Esho RV, Oyejide OS.   +4 more
europepmc   +1 more source

Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis. [PDF]

BMC Cardiovasc Disord
Hajishah H, Kazemi D, Safaee E, Amini MJ, Peisepar M, Tanhapour MM, Tavasol A.   +6 more
europepmc   +1 more source

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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
openaire   +3 more sources

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
openaire   +1 more source

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
openaire   +2 more sources

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.
openaire   +2 more sources