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7 articles for “Alternating Least Squares (ALS)”
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Impact of ML in Evolution of Recommender Systems: A Review
Abstract: Recommender systems have become indispensable tools in today's digital era, catering to the overwhelming demand for personalized recommendations in various domains such as e-commerce, content streaming, and social media. The continuous growth of data and user interactions has necessitated the integration of advanced machine learning (ML) techniques to enhance the accuracy and efficiency of these systems. This study provides a thorough analysis of current developments in machine learning methods used …
Published in E-Commerce for Future & Trends · Vol. 10, Issue 2, 2023 · pp. 26–32 Read article
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A Comparison among Twenty-Seven Normality Tests
Abstract: This paper studies and compares the power of 27 normality tests via the Monte Carlo simulation of sample data generated from symmetric three short-tailed and three long-tailed, asymmetric three short-tailed and three long-tailed distributions under different sample sizes by using R codes. Our simulation results showed that for symmetric short-tailed Uniform (0, 1), t (10) and Beta (4, 4) distributions showed that the Agostino-Pearson K2, Jarque Bera and Kurtosis tests …
Published in Research & Reviews : Journal of Statistics · Vol. 8, Issue 3, 2019 · pp. 41–59 Read article
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Cointegration and Error Correaction Modeling for BSE and NSE Stock Prices Time Series Data
Abstract: AbstractEconometrician always have been observed that most of the economics time series are non-stationary. The ordinary least square technique could be applied to estimate the model parameters only if the variables have been found to be stationary, i.e., they do not have unit roots. Otherwise, an alternative approach has to be followed, which is ‘co-integration’. Co-integration is a method of finding out the long-term relationship between economic variables under consideration. …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 4, Issue 3, 2017 · pp. 30–41 Read article
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The Fundamental Premises of Efficient Linear Regression and Applying Regression
Abstract: Linear regression is a statistical technique for estimating the value of a dependent variable from an independent variable. Linear regression is a way to assess how two variables are related. A dependent variable is predicted using this modelling technique based on one or more independent factors. Many analyses are based on linear regression. Sometimes the data must be changed to satisfy the needs of the analysis, or extra room must …
Published in Trends in Machine design · Vol. 9, Issue 4, 2022 Read article
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Performance of Some Modified Ordinary Ridge Regression Estimators
Abstract: In multiple linear regressions, if the data suffer from severe multicollinearity, then the ordinary least squares (OLS) method become more sensitive to it, and in such a case OLS could yield wrong sign for some of the regression coefficients. Therefore, when such a situation arises, we could use one of the biased regression methods viz., ridge regression, principal component regression, and so on, as an alternative method to OLS. This …
Published in Research & Reviews : Journal of Statistics · Vol. 11, Issue 4, 2022 Read article
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Machine Learning-Based Channel Estimation in 5G, Beyond-5G, and 6G Networks: Recent Advances and Future Directions
Abstract: Accurate channel estimation is one of the most fundamental challenges in modern wireless communication systems. In fifth- generation (5G) New Radio (NR) and emerging sixth-generation (6G) networks, precise knowledge of the wireless channel is essential for achieving reliable data transmission, high spectral efficiency, and low Bit Error Rate (BER). Conventional estimation techniques such as Least Squares (LS) and Minimum Mean Square Error (MMSE) rely on mathematical channel models and predefined …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 2, 2026 Read article
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Defining Regression Polynomials with Process Similarity Criteria
Abstract: Highly accurate monitoring and prediction is essential in detecting abnormality events and forecasting demands in supply chains. This investigation provides a computational procedure for defining polynomials that satisfy similarity criteria in the monitoring process for a polynomial of any degree. It provides an example of using algorithms to predict transient thermal processes in electronic devices. The second-degree polynomial defined with this algorithm is compared with a polynomial defined using a …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 10, Issue 2, 2023 · pp. 13–20 Read article