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Pool-based active learning based on incremental decision tree
2010 International Conference on Machine Learning and Cybernetics, 2010 The pool-based active learning intends to collect the samples into the pool firstly, and selects the best informative sample from it which has no label to add into the training sets for updating the classifier secondly. This paper proposed a new method based on the incremental decision tree algorithm to measure the ambiguity of the unlabeled samples ...
Shuo Wang +3 more
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Multi-resident Activity Recognition Using Incremental Decision Trees
International Conference on Adaptive and Intelligent Systems, 2014 The present paper proposes the application of decision trees to model activities of daily living in a multi-resident context. An extension of ID5R, called E-ID5R, is proposed. It augments the leaf nodes and allows such nodes to be multi-labeled. E-ID5R induces a decision tree incrementally to accommodate new instances and new activities as they become ...
Markus Prossegger, Abdelhamid Bouchachia
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Research on incremental decision tree algorithm
Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology, 2011For 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.
Q. Chi
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Incremental Fuzzy Decision Trees
, 2002 We present a new classification algorithm that combines three properties: It generates decision trees, which proved a valuable and intelligible tool for classification and generalization of data; it utilizes fuzzy logic, that provides for a fine grained description of classified items adequate for human reasoning; and it is incremental, allowing rapid ...
Marina Guetova +2 more
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Multi-objective Optimization for Incremental Decision Tree Learning
International Conference on Data Warehousing and Knowledge Discovery, 2012Decision 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
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Decision tree usage for incremental parametric speech synthesis
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014Human 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 ...
Timo Baumann
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A New Incremental Learning Technique For Decision Trees With Thresholds
SPIE Proceedings, 1989 This paper presents some basic algorithms for manipulating decision trees with thresholds. The algorithms are based on discrete decision theory. This algebraic approach to discrete decision theory, in particular, provides syntactic techniques for reducing the size of decision trees.
Jacques Robin, B. Cockett, Yunzhou Zhu
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On Incremental Learning for Gradient Boosting Decision Trees
Neural Processing Letters, 2019Boosting algorithms, as a class of ensemble learning methods, have become very popular in data classification, owing to their strong theoretical guarantees and outstanding prediction performance. However, most of these boosting algorithms were designed for static data, thus they can not be directly applied to on-line learning and incremental learning ...
Yuan Zhang +5 more
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An Increment Decision Tree Algorithm for Streamed Data
2015 IEEE Trustcom/BigDataSE/ISPA, 2015 Incremental (online) learning algorithms are methods for on-demand classification process from continuous streams of data. The main purpose is to deal with the classification task when original dataset is too large to process or when new instances of data arrive at any time.
Dariusz Jankowski, Konrad Jackowski
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