Acquaintances in Machine Learning - Considering the Integration of Tech-Facts

Authors

  • Pramod Talole  Department of Information Technology, Anuradha Engineering College, Chikhli, Maharashtra, India
  • Dipak Mathe  Department of Information Technology, Anuradha Engineering College, Chikhli, Maharashtra, India
  • Yogeshwari Rajure  Department of Information Technology, Anuradha Engineering College, Chikhli, Maharashtra, India

Keywords:

Machine Learning, Evaluation, Classification, Taxonomy, Structured, Potential.

Abstract

Contempt the good successes of machine learning, it can have its limits when handling insufficient training data. A potential solution is to include additional knowledge into the training process which results in the thought of assured machine learning. The topic covers search survey and structured overview of varied solutions in this field. This aims to determine taxonomy which may function a classification framework that considers the type of extra knowledge, its representation, and its combination into the machine learning pipeline. The evaluation of various papers on the bases of the taxonomy uncovers key ways of this field.

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International Journal of Scientific Research in Science and Technology

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Published

2020-02-17

Issue

Volume 5, Issue 6, January-February-2020

Section

Research Articles

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Copyright (c) IJSRST

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Pramod Talole, Dipak Mathe, Yogeshwari Rajure, " Acquaintances in Machine Learning - Considering the Integration of Tech-Facts, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 5, Issue 6, pp.273-278, January-February-2020.