Incremental learning algorithms and applications
Incremental learning refers to learning from streaming data, which arrive over time, with
limited memory resources and, ideally, without sacrificing model accuracy. This setting fits …
limited memory resources and, ideally, without sacrificing model accuracy. This setting fits …
Research on time series data mining algorithm based on Bayesian node incremental decision tree
S **ngrong - Cluster Computing, 2019 - Springer
Aiming at the shortage of classic ID3 decision tree and C4. 5 decision tree algorithm in
ability of time series data mining, this paper increases Bayesian classification algorithm in …
ability of time series data mining, this paper increases Bayesian classification algorithm in …
An adapted incremental graded multi-label classification model for recommendation systems
Graded multi-label classification (GMLC) is the task of assigning to each data a set of
relevant labels with corresponding membership grades. This paper is interested in GMLC for …
relevant labels with corresponding membership grades. This paper is interested in GMLC for …
Simple ranking method using reference profiles: incremental elicitation of the preference parameters
Abstract The Simple Ranking Method using Reference Profiles (or SRMP) is a Multi-Criteria
Decision Aiding technique based on the outranking paradigm, which allows to rank decision …
Decision Aiding technique based on the outranking paradigm, which allows to rank decision …
Splitting with confidence in decision trees with application to stream mining
Decision tree classifiers are a widely used tool in data stream mining. The use of confidence
intervals to estimate the gain associated with each split leads to very effective methods, like …
intervals to estimate the gain associated with each split leads to very effective methods, like …
Confidence decision trees via online and active learning for streaming data
Decision tree classifiers are a widely used tool in data stream mining. The use of confidence
intervals to estimate the gain associated with each split leads to very effective methods, like …
intervals to estimate the gain associated with each split leads to very effective methods, like …
Preliminary big data analytics of hepatitis disease by random forest and SVM using r-tool
PRV Lakshmi, G Shwetha… - 2017 Third International …, 2017 - ieeexplore.ieee.org
In the growing era of technology, concentration is on the analysis of large amount of
structured and unstructured data. The processing applications are inadequate to deal with …
structured and unstructured data. The processing applications are inadequate to deal with …
A comparison between person re-identification approaches
B Hadjkacem, W Ayedi, M Abid - 2016 Third International …, 2016 - ieeexplore.ieee.org
In this paper, we presented a comparison between different approaches of person re-
identification in camera network based on the-state-of-the-art. We studied the different …
identification in camera network based on the-state-of-the-art. We studied the different …
Analysis of medical image and health informatics using bigdata
G Shwetha, PRV Lakshmi… - 2017 Third International …, 2017 - ieeexplore.ieee.org
In the growing era of technology, are resulting in large amount of structured and
unstructured data. The processing applications are inadequate to deal with these data are …
unstructured data. The processing applications are inadequate to deal with these data are …
Analytical split value calculation for numerical attributes in hoeffding trees with misclassification-based impurity
Hoeffding tree is a method to incrementally build decision trees. A common approach to
handle numerical attributes in Hoeffding trees is to represent their sufficient statistics as …
handle numerical attributes in Hoeffding trees is to represent their sufficient statistics as …