An Efficient Approach for Supervised Learning Algorithms Using Different Data Mining Tools for Spam Categorization

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

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Machine Learning and Data Mining in Bioinformatics

Handbook of Research on Innovations in Database Technologies and Applications ◽

10.4018/978-1-60566-242-8.ch066 ◽

2009 ◽

pp. 612-621 ◽

Cited By ~ 2

Author(s):

George Tzanis ◽

Christos Berberidis ◽

Ioannis Vlahavas

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Data Mining ◽

Reinforcement Learning ◽

Supervised Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

The World ◽

Supervised Learning Algorithms ◽

Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

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Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set

Periodicals of Engineering and Natural Sciences (PEN) ◽

10.21533/pen.v7i2.483 ◽

2019 ◽

Vol 7 (2) ◽

pp. 438

Author(s):

Saadaldeen Rashid Ahmed Ahmed ◽

Israa Al Barazanchi ◽

Ammar Mhana ◽

Haider Rasheed Abdulshaheed

Keyword(s):

Lung Cancer ◽

Data Mining ◽

Supervised Learning ◽

Learning Algorithms ◽

Cancer Classification ◽

Data Set ◽

Supervised Learning Algorithms ◽

Using Data

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Big Data Mining Algorithms

Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management ◽

10.4018/978-1-7998-3479-3.ch052 ◽

2021 ◽

pp. 768-777

Author(s):

M. Govindarajan

Keyword(s):

Machine Learning ◽

Data Mining ◽

Big Data ◽

Unsupervised Learning ◽

Supervised Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Data Sets ◽

Big Data Mining ◽

Supervised Learning Algorithms

Big data mining involves knowledge discovery from these large data sets. The purpose of this chapter is to provide an analysis of different machine learning algorithms available for performing big data analytics. The machine learning algorithms are categorized in three key categories, namely, supervised, unsupervised, and semi-supervised machine learning algorithm. The supervised learning algorithms are trained with a complete set of data, and thus, the supervised learning algorithms are used to predict/forecast. Example algorithms include logistic regression and the back propagation neural network. The unsupervised learning algorithms starts learning from scratch, and therefore, the unsupervised learning algorithms are used for clustering. Example algorithms include: the Apriori algorithm and K-Means. The semi-supervised learning combines both supervised and unsupervised learning algorithms. The semi-supervised algorithms are trained, and the algorithms also include non-trained learning.

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