Data Classification: Algorithms and Applications

By Stephen Coggeshall, Ralf Klinkenberg. 2014

376 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/82076c288b729fd87050e27a74760ad5f6e164bf

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Citations in the corpus

Star-galaxy Classification Using Deep Convolutional Neural Networks (2016, methodology): “To compare the performance of ConvNets with machine learning algorithms that use standard photometric features, we use a machine learning framework called Trees for Probabilistic Classifications (TPC). TPC is a parallel, supervised machine learning algorithm that uses prediction trees and random for”
Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, background): “The general process is: select a subset, evaluate the subset according to the prediction performance, select a new subset, and continue to evaluate until the expected quality is reached [75].”
Recommender Systems (2016, unknown): “Association rule-based recommender systems can be viewed as generalizations of rulebased systems that are used commonly for the classification problem [18].”
Incremental on-line learning: A review and comparison of state of the art algorithms (2018, background): “Incremental learning is more frequently treated in the setting of streaming scenarios [17, 18], although most of the work particularly targets concept drift [19, 20, 6].”
A survey on missing data in machine learning (2021, background): “The major component in stacking is the optimal features and the algorithm for learning at the meta-level [120].”
Adversarial Attacks on Neural Networks for Graph Data (2018, methodology): “data where so-called network effects such as homophily (that is, nodes tend to connect to nodes similar to themselves) [London and Getoor, 2014] support the classification.”
Neural Networks and Deep Learning (2018, unknown): “The reader is recommended to refer to [2, 3, 40, 177] for basic knowledge on machine learning methods.”