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A survey on ensemble learning
Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …
learning methods may fail to obtain satisfactory performances when dealing with complex …
An empowered AdaBoost algorithm implementation: A COVID-19 dataset study
The Covid-19 outbreak, which emerged in 2020, became the top priority of the world. The
fight against this disease, which has caused millions of people's deaths, is still ongoing, and …
fight against this disease, which has caused millions of people's deaths, is still ongoing, and …
Fault detection and classification based on co-training of semisupervised machine learning
This paper presents a semisupervised machine learning approach based on co-training of
two classifiers for fault classification in both the transmission and the distribution systems …
two classifiers for fault classification in both the transmission and the distribution systems …
An overview of audio event detection methods from feature extraction to classification
Audio streams, such as news broadcasting, meeting rooms, and special video comprise
sound from an extensive variety of sources. The detection of audio events including speech …
sound from an extensive variety of sources. The detection of audio events including speech …
Speech act identification using semantic dependency graphs with probabilistic context-free grammars
JF Yeh - ACM Transactions on Asian and Low-Resource …, 2016 - dl.acm.org
We propose an approach for identifying the speech acts of speakers' utterances in
conversational spoken dialogue that involves using semantic dependency graphs with …
conversational spoken dialogue that involves using semantic dependency graphs with …
Adaptive semi-supervised classifier ensemble for high dimensional data classification
High dimensional data classification with very limited labeled training data is a challenging
task in the area of data mining. In order to tackle this task, we first propose a feature …
task in the area of data mining. In order to tackle this task, we first propose a feature …
Progressive semisupervised learning of multiple classifiers
Semisupervised learning methods are often adopted to handle datasets with very small
number of labeled samples. However, conventional semisupervised ensemble learning …
number of labeled samples. However, conventional semisupervised ensemble learning …
Multiview semi-supervised learning with consensus
Obtaining high-quality and up-to-date labeled data can be difficult in many real-world
machine learning applications. Semi-supervised learning aims to improve the performance …
machine learning applications. Semi-supervised learning aims to improve the performance …
Joint consensus and diversity for multi-view semi-supervised classification
As data can be acquired in an ever-increasing number of ways, multi-view data is becoming
more and more available. Considering the high price of labeling data in many machine …
more and more available. Considering the high price of labeling data in many machine …
Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review
This study presents a comprehensive multidisciplinary review of autonomous monitoring
and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies …
and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies …