Results 211 to 220 of about 112,281 (246)

Efflcient incremental decision tree generation for embedded applications

IEEE Conference on Cybernetics and Intelligent Systems, 2004., 2005
This paper describes a frequency table-based decision tree algorithm for embedded applications. The table contains a compact statistical representation of the training set feature vectors and can be used in conjunction with a variety of learning methods.
Erick Swere, David Mulvaney, Ian Sillitoe   +2 more
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Research on incremental decision tree algorithm

Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology, 2011
For data analysis of increase rapidly customer behavior, Web log analysis, network intrusion detection systems and other online classification system, how to quickly adapt to new samples is the key to ensure proper classification and sustainable operation.
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Fuzzy Hoeffding Decision Trees for Incremental and Interpretable Predictions of Students' Outcomes

2022
Pre-Print of the ...
Gabriella Casalino, Pietro Ducange, Michela Fazzolari, Riccardo Pecori   +3 more
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INCREMENTAL LEARNING DECISION TREE ALGORITHM FOR KNOWLEDGE DISCOVERY

PONTE International Scientific Researchs Journal, 2016
A prominent learning discovery procedure is Data Mining. Decision trees are of the basic and intense decision making models in data mining. A single constraint in decision trees is the unpredictability and error rate. Motivated by human learning techniques, we suggest a decision tree structure which impersonates human adapting by performing consistent ...
Mohammed Ali Hussain, Mohammed Moulana
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Incremental Induction of Belief Decision Trees in Averaging Approach

2014
This paper extends the belief decision tree learning method to an incremental mode where the tree structure could change when new data come. This so-called incremental belief decision tree is a new classification method able to learn new instances incrementally, by updating and restructuring an existing belief decision tree.
Salsabil Trabelsi, Mouna El Aroui, Zied Elouedi   +2 more
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Multi-objective Optimization for Incremental Decision Tree Learning

2012
Decision tree learning can be roughly classified into two categories: static and incremental inductions. Static tree induction applies greedy search in splitting test for obtaining a global optimal model. Incremental tree induction constructs a decision model by analyzing data in short segments; during each segment a local optimal tree structure is ...
Yain-Whar Si, Hang Yang, Simon Fong
openaire   +2 more sources

Decision tree usage for incremental parametric speech synthesis

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
Human speakers plan and deliver their utterances incrementally, piece-by-piece, and it is obvious that their choice regarding phonetic details (and the details' peculiarities) is rarely determined by globally optimal solutions. In contrast, parametric speech synthesizers use a full-utterance context when optimizing vocoding parameters and when ...
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CONSOLIDATING A HEURISTIC FOR INCREMENTAL DECISION TREE LEARNING THROUGH ASYMPTOTIC ANALYSIS

International Journal on Artificial Intelligence Tools, 2011
This paper addresses stability issues in incremental induction of decision trees. Stability problems arise when an induction algorithm must revise a decision tree very often and oscillations between similar concepts decrease learning speed. We review a heuristic that solves this problem and subsequently employ asymptotic analysis to approximate the ...
Athanasios Papagelis, Yannis C. Stamatiou, Dimitris Kalles   +2 more
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Safety Content Filtering Based on Incremental Induction of Decision Trees

2012 Sixth International Conference on Internet Computing for Science and Engineering, 2012
This paper presents a novel method of safety content filtering exploiting incremental induction of decision trees. Via defining an appropriate attribute space, this approach adapts the security content filtering task to online incremental induction of decision trees learning framework.
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Sample selection based on multiple incremental decision trees in BSP programming library

2012 International Conference on Machine Learning and Cybernetics, 2012
The sample selection is a key in the active learning, because it intends to select the best informative sample which has no label from the pool or online. And then the selected sample needs to be added into the training sets for updating the classifier.
Xue-Zheng Wang, Jianjian Wang, Yi Wang, Shuo Wang   +3 more
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