A Conjectural Study on Machine Learning Algorithms

被引：3

作者：

Sankar, Abijith ^{[1
]}

Bharathi, P. Divya ^{[1
]}

Midhun, M. ^{[1
]}

Vijay, K. ^{[1
]}

Kumar, T. Senthil ^{[1
]}

机构：

[1] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Dept Comp Sci & Engn, Coimbatore, Tamil Nadu, India

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SOFT COMPUTING SYSTEMS, ICSCS 2015, VOL 1 | 2016年 / 397卷

关键词：

Machine learning algorithms; Supervised learning; Unsupervised learning; Bagging; Boosting; KNN; Random forests; Logistic regression; Decision trees; Naive bayes; k-Means clustering; Partitional clustering; Divisive clustering; Hierarchical clustering; Agglomerative clustering;

D O I：

10.1007/978-81-322-2671-0_10

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Artificial Intelligence, a field which deals with the study and design of systems, which has the capability of observing its environment and does functionalities which aims at maximizing the probability of its success in solving problems. AI turned out to be a field which captured wide interest and attention from the scientific world, so that it gained extraordinary growth. This in turn resulted in the increased focus on a field-which deals with developing the underlying conjectures of learning aspects and learning machines-machine learning. The methodologies and objectives of machine learning played a vital role in the considerable progress gained by AI. Machine learning aims at improving the learning capabilities of intelligent systems. This survey is aimed at providing a theoretical insight into the major algorithms that are used in machine learning and the basic methodology followed in them.

引用

页码：105 / 116

页数：12

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