Integration of machine learning and human learning for training optimization in robust linear regression

Silvopasture systems combine tree and livestock production to minimize market risk and enhance ecological services. Our objective was to explore and develop a method for identifying driving factors linked to productivity in a silvopastoral system using machine learning. A multi-variable approach was used to detect factors that affect system-level output (i.e., plant production (tree and forage), soil factors, and animal response based on grazing preference). Variables from a three-year (2017–2019) grazing study, including forage, tree, soil, and terrain attribute parameters, were analyzed. Hierarchical variable clustering and random forest model selected 10 important variables for each of four major clusters. A stepwise multiple linear regression and regression tree approach was used to predict cattle grazing hours per animal unit (h ha−1 AU−1) using 40 variables (10 per cluster) selected from 130 total variables. Overall, the variable ranking method selected more weighted variables for systems-level analysis. The regression tree performed better than stepwise linear regression for interpreting factor-level effects on animal grazing preference. Cattle were more likely to graze forage on soils with Cd levels <0.04 mg kg−1 (126% greater grazing hours per AU), soil Cr <0.098 mg kg−1 (108%), and a SAGA wetness index of <2.7 (57%). Cattle also preferred grazing (88%) native grasses compared to orchardgrass (Dactylis glomerata L.). The result shows water flow within the landscape position (wetness index), and associated metals distribution may be used as an indicator of animal grazing preference. Overall, soil nutrient distribution patterns drove grazing response, although animal grazing preference was also influenced by aboveground (forage and tree), soil, and landscape attributes. Machine learning approaches helped explain pasture use and overall drivers of grazing preference in a multifunctional system.

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Machine Learning Based Device Simulation Using Multi-variable Non-linear Regression to Assess the Impact of Device Parameter Variability on Threshold Voltage of Double Gate-All-Around (DGAA) MOSFET

2020 IEEE 2nd International Conference on Circuits and Systems (ICCS) ◽

10.1109/iccs51219.2020.9336608 ◽

2020 ◽

Author(s):

Sandeep Moparthi ◽

Chandan Yadav ◽

Gopi Krishna Saramekala ◽

Pramod Kumar Tiwari

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Threshold Voltage ◽

Device Simulation ◽

Double Gate ◽

Device Parameter ◽

Non Linear ◽

The Impact

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Study on Influence of Range of Data in Concrete Compressive Strength with Respect to the Accuracy of Machine Learning with Linear Regression

Applied Sciences ◽

10.3390/app11093866 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3866

Author(s):

Jun-Ryeol Park ◽

Hye-Jin Lee ◽

Keun-Hyeok Yang ◽

Jung-Keun Kook ◽

Sanghee Kim

Keyword(s):

Machine Learning ◽

Compressive Strength ◽

Linear Regression ◽

Coarse Aggregate ◽

Learning Algorithm ◽

Linear Regression Analysis ◽

Aggregate Size ◽

Training Dataset ◽

Machine Learning Algorithm ◽

Concrete Compressive Strength

This study aims to predict the compressive strength of concrete using a machine-learning algorithm with linear regression analysis and to evaluate its accuracy. The open-source software library TensorFlow was used to develop the machine-learning algorithm. In the machine-earning algorithm, a total of seven variables were set: water, cement, fly ash, blast furnace slag, sand, coarse aggregate, and coarse aggregate size. A total of 4297 concrete mixtures with measured compressive strengths were employed to train and testing the machine-learning algorithm. Of these, 70% were used for training, and 30% were utilized for verification. For verification, the research was conducted by classifying the mixtures into three cases: the case where the machine-learning algorithm was trained using all the data (Case-1), the case where the machine-learning algorithm was trained while maintaining the same number of training dataset for each strength range (Case-2), and the case where the machine-learning algorithm was trained after making the subcase of each strength range (Case-3). The results indicated that the error percentages of Case-1 and Case-2 did not differ significantly. The error percentage of Case-3 was far smaller than those of Case-1 and Case-2. Therefore, it was concluded that the range of training dataset of the concrete compressive strength is as important as the amount of training dataset for accurately predicting the concrete compressive strength using the machine-learning algorithm.

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Beneficial and harmful explanatory machine learning

Machine Learning ◽

10.1007/s10994-020-05941-0 ◽

2021 ◽

Author(s):

Lun Ai ◽

Stephen H. Muggleton ◽

Céline Hocquette ◽

Mark Gromowski ◽

Ute Schmid

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Beneficial Effect ◽

Empirical Evidence ◽

Task Performance ◽

Human Performance ◽

Human Learning ◽

Science Literature ◽

Definition Of ◽

Self Learning

AbstractGiven the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong machine learning (USML). USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance. A recent paper demonstrates the beneficial effect of a machine learned logic theory for a classification task, yet no existing work to our knowledge has examined the potential harmfulness of machine’s involvement for human comprehension during learning. This paper investigates the explanatory effects of a machine learned theory in the context of simple two person games and proposes a framework for identifying the harmfulness of machine explanations based on the Cognitive Science literature. The approach involves a cognitive window consisting of two quantifiable bounds and it is supported by empirical evidence collected from human trials. Our quantitative and qualitative results indicate that human learning aided by a symbolic machine learned theory which satisfies a cognitive window has achieved significantly higher performance than human self learning. Results also demonstrate that human learning aided by a symbolic machine learned theory that fails to satisfy this window leads to significantly worse performance than unaided human learning.

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QUBO formulations for training machine learning models

Scientific Reports ◽

10.1038/s41598-021-89461-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Prasanna Date ◽

Davis Arthur ◽

Lauren Pusey-Nazzaro

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Large Scale ◽

Support Vector ◽

Quantum Computers ◽

Np Hard ◽

Learning Models ◽

Moore’S Law ◽

Moore's Law ◽

Machine Learning Models

AbstractTraining machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.

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