An Introduction to Machine Learning

By Miroslav Kubat. 2017

4,465 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Optimization & Training

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Citations in the corpus

Machine Learning Methods That Economists Should Know About (2019, methodology): “[2009], which is a more comprehensive text from a statistics perspective, and Burkov [2019] which is a very accessible introduction, Alpaydin [2009], and Knox [2018], which all take more of a computer science perspective.”
Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, methodology): “Recent developments in a different ﬁeld, that of machine learning (Michalski et al., 2013; Alpaydin, 2014) and neural learning (LeCun et al., 2015), also suggest the importance of learning from large input-output data.”
Comparison of Naive Bayes, Random Forest, Decision Tree, Support Vector Machines, and Logistic Regression Classifiers for Text Reviews Classification (2017, methodology): “Classification methods are unique data-processing features of machine learning (Alpaydin, 2010) and allows to run multi-class text-classification.”
Machine learning in manufacturing: advantages, challenges, and applications (2016, methodology): “These NN play an important role in today’s ML research (Nilsson, 2005).”
Grant-Free Non-Orthogonal Multiple Access for IoT: A Survey (2019, background): “Recent research continues to conﬁrm the powerful capabilities of ML technologies [179] in enhancing the efﬁciency of transmitter/receiver designs in wireless communications.”
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey (2018, neutral): “However, the learning for the ﬁnal classiﬁer is achieved through the weighted votes of the classiﬁers [165], [166].”
Computational Principles of Supervised Learning in the Cerebellum (2018, neutral): “…processor until it learns to generate the desired response. tuning is achieved by adjusting connection weights in artificial neural networks (Alpaydin 2014, Hagan et al. 2014, Jenkins et al. 1996) or by adjusting the strength of synapses or other properties of neurons in the brain (i.e.,…”
Machine Learning: Algorithms and Applications (2016, methodology): “→ A process by which a system improves its performance [Simon, 1983] → Any computer program that improves its performance at some task through experience [Mitchell, 1997] → Programming computers to optimize a performance criterion using example data or past experience [Alpaydin, 2004]”
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, methodology): “ing theory and its classical concepts, please refer to [73]–[76].”
An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance (2021, methodology): “Reinforcement learning draws from behaviourism and is a psychological perspective of human behaviour (Lison, 2015).”
Process Mining (2016, methodology): “Different learning paradigms can be used to train the neural network: supervised learning, unsupervised learning, and reinforcement learning [9, 102].”
An efficient distributed learning algorithm based on effective local functional approximations (2013, background): “It can be shown (Smola and Vishwanathan, 2008) that, if f is σ -strongly convex, then f satisﬁes assumption A2.”
Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda (2021, background): “…and technologies that comprise an AI system may include, but are not limited to: machine learning, including supervised and unsupervised learning (Smola & Vishwanathan, 2008; UNESCO, 2020); Artificial Neural Networks (Krenker, Bester, & Kos, 2011); fuzzy logic (Klir & Yuan, 1995; Yen & Langari,…”
kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data (2017, methodology): “This is the most commonly used metric for assessing the performance of classifiers for years in standard classification ([38] [39]).”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, methodology): “In contrast, the training of unsupervised learning tasks is done without labeled data [65].”
Sociology in the Era of Big Data: The Ascent of Forensic Social Science (2015, neutral): “For lack of a better term, many of these algorithms adopt a machine learning approach, albeit one that is often augmented with simulation modeling (Alpaydin 2004).”
Single Subject Prediction of Brain Disorders in Neuroimaging: Promises and Pitfalls (2016, background): “, 2008) and in general (Klöppel et al., 2012; Lemm et al., 2011; Orrù et al., 2012) as well as modality-specific reviews such as machine learning based on fMRI (Sundermann et al.”
Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions (2023, methodology): “It may be utilised in cameras for object detection, picture, colour, and pattern recognition, data collection, data sorting, and audio-to-text translation [7].”