scholarly journals Determining the parameters of high amplification microlensing events by means of statistical machine learning techniques

2016 ◽  
Vol 12 (S325) ◽  
pp. 213-216
Author(s):  
Elena Fedorova
Keyword(s):  
Machine Learning ◽  
Dark Matter ◽  
Machine Learning Techniques ◽  
Statistical Machine Learning ◽  
Density Profiles ◽  
Important Clue ◽  
Gravitational Microlensing ◽  
Learning Techniques

AbstractStrong gravitational microlensing (GM) events provide us a possibility to determine both the parameters of microlensed source and microlens. GM can be an important clue to understand the nature of dark matter on comparably small spatial and mass scales (i.e. substructure), especially when speaking about the combination of astrometrical and photometrical data about high amplification microlensing events (HAME). In the same time, fitting of HAME lightcurves of microlensed sources is quite time-consuming process. That is why we test here the possibility to apply the statistical machine learning techniques to determine the source and microlens parameters for the set of HAME lightcurves, using the simulated set of amplification curves of sources microlensed by point masses and clumps of DM with various density profiles.

scholarly journals Analytical Statistics Techniques of Classification and Regression in Machine Learning

2020 ◽  
Author(s):  
Pramod Kumar ◽  
Sameer Ambekar ◽  
Manish Kumar ◽  
Subarna Roy
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Machine Learning Techniques ◽  
Statistical Machine Learning ◽  
Building Models ◽  
Learning Techniques ◽  
Implementation Techniques ◽  
Regression Techniques ◽  
The Common ◽  
Classification And Regression

This chapter aims to introduce the common methods and practices of statistical machine learning techniques. It contains the development of algorithms, applications of algorithms and also the ways by which they learn from the observed data by building models. In turn, these models can be used to predict. Although one assumes that machine learning and statistics are not quite related to each other, it is evident that machine learning and statistics go hand in hand. We observe how the methods used in statistics such as linear regression and classification are made use of in machine learning. We also take a look at the implementation techniques of classification and regression techniques. Although machine learning provides standard libraries to implement tons of algorithms, we take a look on how to tune the algorithms and what parameters of the algorithm or the features of the algorithm affect the performance of the algorithm based on the statistical methods.

scholarly journals Development of a Simulation Prediction System Using Statistical Machine Learning Techniques

2016 ◽  
Vol 5 (11) ◽  
pp. 593-606
Author(s):  
Ki Yong Lee ◽  
YoonJae Shin ◽  
YeonJeong Choe ◽  
SeonJeong Kim ◽  
Young-Kyoon Suh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Prediction System ◽  
Statistical Machine Learning ◽  
Learning Techniques

scholarly journals Don’t Dismiss Logistic Regression: The Case for Sensible Extraction of Interactions in the Era of Machine Learning

2019 ◽  
Author(s):  
Joshua J. Levy ◽  
A. James O’Malley
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Model Building ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Statistical Machine Learning ◽  
Forest Model ◽  
Learning Techniques ◽  
Modeling Techniques

AbstractBackgroundMachine learning approaches have become increasingly popular modeling techniques, relying on data-driven heuristics to arrive at its solutions. Recent comparisons between these algorithms and traditional statistical modeling techniques have largely ignored the superiority gained by the former approaches due to involvement of model-building search algorithms. This has led to alignment of statistical and machine learning approaches with different types of problems and the under-development of procedures that combine their attributes. In this context, we hoped to understand the domains of applicability for each approach and to identify areas where a marriage between the two approaches is warranted. We then sought to develop a hybrid statistical-machine learning procedure with the best attributes of each.MethodsWe present three simple examples to illustrate when to use each modeling approach and posit a general framework for combining them into an enhanced logistic regression model building procedure that aids interpretation. We study 556 benchmark machine learning datasets to uncover when machine learning techniques outperformed rudimentary logistic regression models and so are potentially well-equipped to enhance them. We illustrate a software package, InteractionTransformer, which embeds logistic regression with advanced model building capacity by using machine learning algorithms to extract candidate interaction features from a random forest model for inclusion in the model. Finally, we apply our enhanced logistic regression analysis to two real-word biomedical examples, one where predictors vary linearly with the outcome and another with extensive second-order interactions.ResultsPreliminary statistical analysis demonstrated that across 556 benchmark datasets, the random forest approach significantly outperformed the logistic regression approach. We found a statistically significant increase in predictive performance when using hybrid procedures and greater clarity in the association with the outcome of terms acquired compared to directly interpreting the random forest output.ConclusionsWhen a random forest model is closer to the true model, hybrid statistical-machine learning procedures can substantially enhance the performance of statistical procedures in an automated manner while preserving easy interpretation of the results. Such hybrid methods may help facilitate widespread adoption of machine learning techniques in the biomedical setting.

scholarly journals Music Signal Analysis: Regression Analysis

2021 ◽  
Author(s):  
V. N. Aditya Datta Chivukula ◽  
Sri Keshava Reddy Adupala
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Statistical Machine Learning ◽  
Ongoing Research ◽  
Music Signal ◽  
Learning Techniques

Machine learning techniques have become a vital part of every ongoing research in technical areas. In recent times the world has witnessed many beautiful applications of machine learning in a practical sense which amaze us in every aspect. This paper is all about whether we should always rely on deep learning techniques or is it really possible to overcome the performance of simple deep learning algorithms by simple statistical machine learning algorithms by understanding the application and processing the data so that it can help in increasing the performance of the algorithm by a notable amount. The paper mentions the importance of data pre-processing than that of the selection of the algorithm. It discusses the functions involving trigonometric, logarithmic, and exponential terms and also talks about functions that are purely trigonometric. Finally, we discuss regression analysis on music signals.

scholarly journals Improving Explainability of Major Risk Factors in Artificial Neural Networks for Auto Insurance Rate Regulation

Risks ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 126
Author(s):  
Shengkun Xie
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Variable Importance ◽  
Machine Learning Techniques ◽  
Statistical Machine Learning ◽  
Auto Insurance ◽  
Rate Regulation ◽  
Insurance Pricing ◽  
Learning Techniques ◽  
Insurance Rate

In insurance rate-making, the use of statistical machine learning techniques such as artificial neural networks (ANN) is an emerging approach, and many insurance companies have been using them for pricing. However, due to the complexity of model specification and its implementation, model explainability may be essential to meet insurance pricing transparency for rate regulation purposes. This requirement may imply the need for estimating or evaluating the variable importance when complicated models are used. Furthermore, from both rate-making and rate-regulation perspectives, it is critical to investigate the impact of major risk factors on the response variables, such as claim frequency or claim severity. In this work, we consider the modelling problems of how claim counts, claim amounts and average loss per claim are related to major risk factors. ANN models are applied to meet this goal, and variable importance is measured to improve the model’s explainability due to the models’ complex nature. The results obtained from different variable importance measurements are compared, and dominant risk factors are identified. The contribution of this work is in making advanced mathematical models possible for applications in auto insurance rate regulation. This study focuses on analyzing major risks only, but the proposed method can be applied to more general insurance pricing problems when additional risk factors are being considered. In addition, the proposed methodology is useful for other business applications where statistical machine learning techniques are used.

Forecasting stock market crisis events using deep and statistical machine learning techniques

2018 ◽  
Vol 112 ◽  
pp. 353-371 ◽  
Author(s):  
Sotirios P. Chatzis ◽  
Vassilis Siakoulis ◽  
Anastasios Petropoulos ◽  
Evangelos Stavroulakis ◽  
Nikos Vlachogiannakis
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Machine Learning Techniques ◽  
Statistical Machine Learning ◽  
Crisis Events ◽  
Learning Techniques

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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