Main Article Content

Abstract

Machine learning is a branch of manmade brainpower science (artificial intelligence) i.e. structures that can read the details. For example, a typewriter can learn to receive email and determine the difference between spam and non-spam messages with each other. After preparation, the draft can place new messages in their envelopes using the setting. At the moment, we don’t know how to configure PCs keeping in mind the end goal to make people productive. Unless the strategies found are effective for certain purposes, they are not suitable for all reasons. For example, machine learning calculations are often used as part of information mines. Indeed, even in areas where data is relevant, these statistics are more efficient and effective than alternative strategies. For example, in news, for example, speech acceptance, counting by visual machine learning has come far more than other conversational strategies. Clearly, it seems that our understanding of PCs will improve step by step. Undoubtedly, one could say that the context of machine learning plays a major role in the field of software engineering and innovation. This paper shows machine learning statistics, including determination strategies, diminishing scales, and deleting nonsensical data

Keywords

Nearest K Neighbor, Decision Tree, Neural Network, Regression, Support Vector Machine.

Article Details

How to Cite
[1]
Ms. Pooja Ambatkar, “MACHINE LEARNING ALGORITHMS IN AI”, IEJRD - International Multidisciplinary Journal, vol. 3, no. 2, p. 7, Mar. 2018.

References

  1. V.Khodadadi et al. Application Of Ants Colony System of Bankruptcy Prediction Of Companies In Tehran Stock Exchange Business Intelligence 2010.
  2. Rahul Reddy Nadikattu, "THE EMERGING ROLE OF ARTIFICIAL INTELLIGENCE IN MODERN SOCIETY", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.4, Issue 4, pp.906-911, December 2016.
  3. A.Aziz & A. Humayon predicting corporate Bankruptcy: whether we stand? Department of Economics, Loughborough University 2002.
  4. Rahul Reddy Nadikattu, "ARTIFICIAL INTELLIGENCE IN CARDIAC MANAGEMENT", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.5, Issue 3, pp.929-938, August-2017.
  5. Rahul Reddy Nadikattu, "THE SUPREMACY OF ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.5, Issue 1, pp.950-954.
  6. Q Yu. Machine Learning of Corporate Bankruptcy Analysis. Information & Computer Science Department, Aalto University, 2013.
  7. Y .Chiang, et al. A Hybrid Approach Of Dea, Rough Set & Support Vector Machines For Prediction of Business Failure 2010.
  8. J Bellovary et. A review on bankruptcy prediction: 1930 to present, Journal of Financial Education 2007.
  9. Rahul Reddy Nadikattu, " CONTENT ANALYSIS OF AMERICAN & INDIAN COMICS ON INSTAGRAM USING MACHINE LEARNING", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.2, Issue 3, pp.86-103, September 2014.
  10. J &rés, M. Landajo, & P. Lorca. Bankruptcy prediction model based on multinorm analysis: An alternative for accounting ratios. Knowledge Based System, 2012.
  11. Ming Yuan Leon & Peter Miu. Hybrid bankruptcy prediction model on accounting ratio based & market based information:Binary quantile regression approach. 2010.
  12. Rahul Reddy Nadikattu, "FUNDAMENTAL APPLICATIONS OF MACHINE LEARNING ACROSS THE GLOBE", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.6, Issue 1, pp.31-40, January 2018.
  13. W Beaver, Financial Ratios of Predictors On Failure, Journal Of Accounting Research 5: 71-111 1996.
  14. E Altman,. Financial Ratios,The Prediction and Discriminant Analysis Of Corporate Bankruptcy, The Journal Of Finance 23(3): 588-608,1969
  15. S ho Et al. Hybrid Approach Based On The Combination Of Variable Selection By Decision Tree & Case-Based Reasoning Using The Mahalanobis Distance- For Bankruptcy Prediction, Expert Systems With Applications 2010.
  16. T Bell, al. Neural Nets Versus Logistic Regression: A Comparison Of Each Model's Ability To Predict Commercial Bank Failures, Proceedings Of The 1990 D&T, University Of Kansas Symposium On Auditing Problems,1990.
  17. M. An&arajan, & A. Anarajan. Bankruptcy Prediction Using Neural Networks, Article In Business Intelligence Techniques: A Perspective From Accounting & Finance,. Germany: SpringerVerlag, 2004.
  18. J. Ohlson .Financial Ratios & The Probabilistic Prediction Of Bankruptcy, Journal Of Accounting Research 18(1): 109-131, 1980.
  19. Xu, Wang Y. Financial Failure Prediction Using Efficiency As A Predictor, Expert Systems With Applications, 366-373, 2009.
  20. G. Zhang, M. Hu, B. Patuwo & D. Indro. Artificial Neural Networks In Bankruptcy Prediction: General Framework & Cross-Validation Analysis, European Journal Of Operational Research 116(1): 16-32, 1999.
  21. T. Shumway. Forecasting bankruptcy more accurately: A simple hazard model.Journal of Business, 1:573–593, 1987.
  22. D. A. Hensher & S. Jones. Forecasting corporate bankruptcy: Optimizing the performance of the mixed logit model. Abacus, 43(3):241–364, 2007.
  23. S. Canbas, A. Cabuk, & S. B. Kilic. Prediction of commercial bank failure via multivariate statistical analysis of financial structure: The Turkish case. European Journal of Operational Research, 1:528–546, 2005.
  24. H. Frydman, E.I. Altman, & D. Kao. Introducing recursive partitioning for financial classification: The case of financial distress. Journal of Finance , 40(1):269–291, 1985.
  25. M. L. Marais, J. Patel, & M. Wolfson. The experimental design of classification models: An application of recursive partitioning & bootstrapping to commercial bank loan classifications. Journal of Accounting Research,22:87–114, 1984.
  26. H. J. Zimmermann. Fuzzy set theory & its applications. Kluwer Academic Publishers, pages 298–319, 1996.
  27. .S. M. Bryant. A case-based reasoning approach to bankruptcy prediction modeling. Intelligent Systems in Accounting, Finance & Management, 6(3):195–214, 1997.
  28. C. Park & I.Han. A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications, 23:255–264, 2002.
  29. K. S. Shin & Y. J. Lee. A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications, 23:312–328, 2002.
  30. F. Varetto. A genetic algorithm application in bankruptcy prediction modeling. Journal of Banking & Finance, 22(10):1421–1439, 1998.
  31. J. H Min & Y. C Lee. Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(5):603–614 2005.
  32. Cielen, L. Peeters, & K. Vanhoof Bankruptcy prediction using a data envelopment analysis. European Journal of Operational Research, 154(3):526–532, 2004.
  33. I. Dimitras, R. Slowinski, R. Susmaga, & C. Zopounidis. Business failure prediction using rough sets. European Journal of Operational Research, 114(2):263–280, 1999.
  34. T. E. Mckee. Developing a bankruptcy prediction model via rough sets theory. Intelligent Systems in Accounting, Finance & Management,9(3):159–173, 2000.
  35. Ph. Simon. Too Big to Ignore: The Business Case for Big Data. Wiley. p. 89, 2013.
  36. W. Yang, B. Yourganov & S. Machine Learning in Medical Imaging, IEEE Signal Processing Magazine, vol. 27, no. 4, 2010.