Machine learning

@article{Dietterich1996MachineL,
  title={Machine learning},
  author={Thomas G. Dietterich},
  journal={ACM Comput. Surv.},
  year={1996},
  volume={28},
  pages={3}
}
Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need… 

A Review on Machine Learning Algorithms

  • A. Raj
  • Computer Science
    International Journal for Research in Applied Science and Engineering Technology
  • 2019
TLDR
The main advantage of using machine learning is that, once an algorithm learns what to do with data, it can do its work automatically.

APPLYING MACHINE LEARNING ALGORITHMS IN SOFTWARE DEVELOPMENT

TLDR
This paper first takes a look at the characteristics and applicability of some frequently utilized machine learning algorithms, then provides formulations of some software development tasks using learning algorithms.

Classifier Systems & Genetic Algorithms

TLDR
The learning classifier system, introduced by Holland and Reitman [1978] is a machine learning system which possesses the salient properties needed to learn in the shape optimization domain.

Learning and Detecting Concept Drift

TLDR
Machine learning is now a core technology in the advanced information society and has been applied to fields such as pattern recognition, search engines, medical support, robot engineering, image processing, and data mining and has achieved significant accomplishments in each field.

Machine-Learning Research Four Current Directions

TLDR
Four topics within machine learning where there has been a lot of recent activity are selected: ensembles of classifiers, methods for scaling up supervised learning algorithms, reinforcement learning, and the learning of complex stochastic models.

A Review on Classification Using Machine Learning

TLDR
This paper examines the various aspects of machine learning with suitable examples and encourages us in discovering answers for some issues in vision, discourse acknowledgment and mechanical technology.

The Relationship of Machine Learning and Data Compression to Natural Language Processing : A Review

TLDR
The state of the art in natural language processing, machine learning, and data compression is reviewed, including what is known about language and learning, both from the computational side of AI, where it is tried to duplicate human intelligence using any engineering technique available, and from the psychological side ofAI, where they try to understand how the human brain does it.

Grammatical inference methodology for control systems

TLDR
The present research intends to evaluate the effectiveness and usefulness of grammatical inference (GI) in control systems and set out the broad methodological lines of the research while stressing the integration of these different approaches into one single unifying entity.

Lifelong machine learning: a paradigm for continuous learning

  • B. Liu
  • Computer Science
    Frontiers of Computer Science
  • 2016
Machine learning (ML) has been instrumental for the advances of both data analysis and artificial intelligence (AI). The recent success of deep learning brings it to a new height. ML algorithms have

Machine Learning for Natural Language Processing

TLDR
This introductory course will cover the basics of Machine Learning and present a selection of widely used al-gorithms, illustrating them with practical applications to Natural Language Processing.
...

References

SHOWING 1-8 OF 8 REFERENCES

Machine-Learning Research Four Current Directions

TLDR
Four topics within machine learning where there has been a lot of recent activity are selected: ensembles of classifiers, methods for scaling up supervised learning algorithms, reinforcement learning, and the learning of complex stochastic models.

Readings in Machine Learning

TLDR
Readings in Machine Learning collects the best of the published machine learning literature, including papers that address a wide range of learning tasks, and that introduce a variety of techniques for giving machines the ability to learn.

Neural Networks for Pattern Recognition

A Theory of the Origins of Human Knowledge

Statistical methods for speech recognition

The speech recognition problem hidden Markov models the acoustic model basic language modelling the Viterbi search hypothesis search on a tree and the fast match elements of information theory the

Unified Theories of Cognition

The Analysis of Time Series: An Introduction. Fifth Edition. Baton Rouge

  • The Analysis of Time Series: An Introduction. Fifth Edition. Baton Rouge
  • 1996

C4.5: Programs for Empirical Learning

  • C4.5: Programs for Empirical Learning
  • 1992