Toward Theory of Applied Learning. What is Machine Learning? (original) (raw)

Machine Learning: Towards an Unified Classification Criteria

Advances in Intelligent Systems and Computing, 2021

In a broad sense, Machine Learning (ML) is the performance optimization in a certain task through computational means, following a certain criterion and using referential data and/or past results from previous iterations. ML is a subset of Artificial Intelligence (AI) and has attracted a substantial amount of research during the last decades. This blooming subject led to the statement of different definitions for classifications, criteria, algorithms and so on. This paper summarizes these different definitions and proposes a homologation between them, providing an unified vision for each definition.

A review on Machine Learning: Application and Algorithms by Diksha, Pallavi and Pankaj Verma

IJRES, 2022

The field of machine learning is introduced at a conceptual level. The main goal of machine learning is how computers automatically learn without any human invention or assistance so that they can adjust their action accordingly. We are discussing mainly three types of algorithms in machine learning and also discussed ML's features and applications in detail. Supervised ML, In this typeof algorithm, the machine applies what it has learned in its past to new data, in which they use labeled examples, so that they predict future events. Unsupervised ML studies how systems can infer a function, so that they can describe a hidden structure from unlabeled data. Reinforcement ML, is a type of learning method, which interacts with its environment, produces action, as well as discovers errors and rewards.

A Comparative Theoretical and Empirical Analysis of Machine Learning Algorithms

2020

With the explosion of data in recent times, Machine learning has emerged as one of the most important methodical approaches to observe significant insights from the vast amount of data. Particularly, it is witnessed that with the alarming rise in the volume of unstructured data on the world wide web, machine learning algorithms can be applied in a wide number of domains to solve various problems related to understanding humans. At the onset, this paper introduces the field of machine learning, classic learning approaches, and machine learning algorithms. A theoretical comparison study of state of the art algorithms is carried based on their logic, characteristics, weaknesses, strengths, and kind of applications in which these algorithms can be used. The study is expected to help buddy researchers who are in the beginning to work in this area.

Conceptual Review on Machine Learning Algorithms for Classification Techniques

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021

Machine leaning is a ground of recent research that officially focuses on the theory, performance, and properties of learning systems and algorithms. It is a extremely interdisciplinary field building upon ideas from many different kinds of fields such as artificial intelligence, optimization theory, information theory, statistics, cognitive science, optimal control, and many other disciplines of science, engineering, and mathematics. Because of its implementation in a wide range of applications, machine learning has covered almost every scientific domain, which has brought great impact on the science and society. It has been used on a variety of problems, including recommendation engines, recognition systems, informatics and data mining, and autonomous control systems. This research paper compared different machine algorithms for classification. Classification is used when the desired output is a discrete label.

A Preliminary Look at Machine Learning

The process of creating machine learning algorithms. This paper delivers the base knowledge needed to understand what machine learning is, the techniques it uses and a look inside the concepts that are required. The process detailed was taken from EliteDataScience's Free 7 Day Crash Course and was re-explained in my own words with some additional knowledge on the concepts explained. The paper includes a brief section on neural networks and why they are used in machine learning today.

Supervised Learning Classification

Learning is a way to develop the skills and knowledge. It is a fundamental property of our brain to acquire the new knowledge and to develop new skill also. The type of learning we have included in our paper are Machine Learning, supervised Learning, and classification of supervised learning. It includes many things about machine learning like their advantages, disadvantages and applications of machine learning (like virtual personal assistance, online media services, E-mail spam). Types of ML included supervised learning, unsupervised learning, and reinforcement learning. There are many SL algorithms which are useful for determining the accuracy of the program but in some case there may be an issues that may occur with supervised learning as we will discuss below in the paper. Algorithm may be used for the determination of accuracy, prediction as well as for better analyses. We use Support vector machine for minimizing the upper bound generalization error. These are directed learning models with related learning calculations that examine data utilization for classification and relapse examination, One another classification method belong to the same family called as Naïve Bayesian network. It basically works on Bayes theorem, it shoulders that the occurrence of the selected features in very category is distinct to the existence of the further attribute. Another supervised technique is Decision Tree in which it identifies the no. of ways to split data based on different condition. The decision tree it divided into two nodes decision node and leaf node each node have different feature and function discussed in below in the paper. The last technique we have discussed is KNN (k-nearest neighbour) in which it determines how many neighbours are to be placed in a single class. We composed the comparison chart on the basis of best algorithm with their accuracy.

A methodological study and analysis of machine learning algorithms

International Journal of Advanced Technology and Engineering Exploration

Machine leaning algorithms have been used in vast area of research including stock market to medical informatics. Support vector machine (SVM), decision tree, random forest, K-nearest neighbors (KNN), naïve Bayes and multilayer perceptron (MLP) are widely used algorithms in different area of data classification. This paper provides theoretical and methodological prospective views based on different machine learning algorithms. For this latest literatures have been discussed with the aim and the scope. Based on the study the area applicability and the gaps have been identified for the future research.

Machine Learning & its Classification Techniques

International Journal of Innovative Technology and Exploring Engineering, 2019

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