70,091 research outputs found
Machine learning methods applied to combined Raman and LIBS spectra: Implications for mineral discrimination in planetary missions
Producción CientíficaThe combined analysis of geological targets by complementary spectroscopic techniques could enhance the characterization of the mineral phases found on Mars. This is indeed the case with the SuperCam instrument onboard the Perseverance rover. In this framework, the present study seeks to evaluate and compare multiple machine learning techniques for the characterization of carbonate minerals based on Raman-LIBS (Laser-Induced Breakdown Spectroscopy) spectroscopic data. To do so, a Ca-Mg prediction curve was created by mixing hydromagnesite and calcite at different concentration ratios. After their characterization by Raman and LIBS spectroscopy, different multivariable machine learning (Gaussian process regression, support vector machines, ensembles of trees, and artificial neural networks) were used to predict the concentration ratio of each sample from their respective datasets. The results obtained by separately analyzing Raman and LIBS data were then compared to those obtained by combining them. By comparing their performance, this work demonstrates that mineral discrimination based on Gaussian and ensemble methods optimized the combine of Raman-LIBS dataset outperformed those ensured by Raman and LIBS data alone. This demonstrated that the fusion of data combination and machine learning is a promising approach to optimize the analysis of spectroscopic data returned from Mars.Agencia Estatal de Investigación, grant (PID2022-142490OB-C32)Ministerio de Economía y Competitividad (MINECO),Grant/Award Number (RDE2018-102600-T
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
Rule-based Machine Learning Methods for Functional Prediction
We describe a machine learning method for predicting the value of a
real-valued function, given the values of multiple input variables. The method
induces solutions from samples in the form of ordered disjunctive normal form
(DNF) decision rules. A central objective of the method and representation is
the induction of compact, easily interpretable solutions. This rule-based
decision model can be extended to search efficiently for similar cases prior to
approximating function values. Experimental results on real-world data
demonstrate that the new techniques are competitive with existing machine
learning and statistical methods and can sometimes yield superior regression
performance.Comment: See http://www.jair.org/ for any accompanying file
Identifying structural changes with unsupervised machine learning methods
Unsupervised machine learning methods are used to identify structural changes
using the melting point transition in classical molecular dynamics simulations
as an example application of the approach. Dimensionality reduction and
clustering methods are applied to instantaneous radial distributions of atomic
configurations from classical molecular dynamics simulations of metallic
systems over a large temperature range. Principal component analysis is used to
dramatically reduce the dimensionality of the feature space across the samples
using an orthogonal linear transformation that preserves the statistical
variance of the data under the condition that the new feature space is linearly
independent. From there, k-means clustering is used to partition the samples
into solid and liquid phases through a criterion motivated by the geometry of
the reduced feature space of the samples, allowing for an estimation of the
melting point transition. This pattern criterion is conceptually similar to how
humans interpret the data but with far greater throughput, as the shapes of the
radial distributions are different for each phase and easily distinguishable by
humans. The transition temperature estimates derived from this machine learning
approach produce comparable results to other methods on similarly small system
sizes. These results show that machine learning approaches can be applied to
structural changes in physical systems
Classic machine learning methods
In this chapter, we present the main classic machine learning methods. A
large part of the chapter is devoted to supervised learning techniques for
classification and regression, including nearest-neighbor methods, linear and
logistic regressions, support vector machines and tree-based algorithms. We
also describe the problem of overfitting as well as strategies to overcome it.
We finally provide a brief overview of unsupervised learning methods, namely
for clustering and dimensionality reduction
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