Machine learning techniques (MLT)
Machine learning techniques for content-based information retrieval
... Machine Learning Techniques for Content-based Information Retrieval Thèse de doctorat de l'Université Paris-Saclay et de l'Université Hassan II de Casablanca préparée à Telecom SudParis / Institut ...
166
Understanding the relationships between aesthetic properties and geometric quantities of free-form surfaces using machine learning techniques
... uses machine learning techniques to identify rules between the user-defined classifications of shapes and the geometric parameters of the underlying free form surfaces and to create an efficient ...
16
Prediction of CAD model defeaturing impact on heat transfer FEA results using machine learning techniques
... processes. Machine Learning tools are widely used in design to address optimization problems [SL1], decision-making problems [L1], shapes recognition [JK1], item recognition or extraction for reuse, ...
7
Supervised Machine Learning Techniques to Detect TimeML Events in French and English
... supervised machine learning framework: TimeML annotated data are provided to train our systems, which are then evaluated on s separate test data ...by machine learning techniques ...
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Estimation of CAD model simplification impact on CFD analysis using machine learning techniques
... that machine learning techniques based on neural networks can be used to predict the impact of simplification processes on CAD model for heat transfer FEA ...
6
An Extensive Investigation of Machine Learning Techniques for Sleep Apnea Screening
... of Machine Learning techniques in the task of determining which factors are more revealing with respect to ...preparation techniques including cleaning of outliers, imputation of missing ...
9
Model-based and machine learning techniques for nonlinear image reconstruction in diffuse optical tomography
... ABSTRACT Diffuse optical tomography (DOT) is a low cost and noninvasive 3D biomedical imaging technique to reconstruct the optical properties of biological tissues. Image reconstruction in DOT is inherently a difficult ...
138
Towards a priori mesh quality estimation using Machine Learning Techniques
... At the end, the normalized distance distribution is not used as it in the learning phase. It is adapted and transformed into four DA meaningful descriptors. Now that all the descriptors have been extracted and ...
11
Applying machine learning techniques to the analysis of policy data of the military health enterprise
... these views have a strong focus on Aim one (readiness), a medium focus on Aims two and three (population health and experience of care), and a weak focus on the remain[r] ...
159
Modeling the power consumption of computing systems and applications through machine learning techniques
... The learning of a model from scratch requires the automatic selection of variables. In this work we proposed a residual based forward selection, i.e. a model starts with a single variable and will include new ...
166
Predicting the global far-infrared SED of galaxies via machine learning techniques
... of machine learn- ing ...a machine learning approach can outperform mod- els (if necessary by learning the di ...fferential). Machine learning also works on any kind of data, and ...
24
Applications of machine learning : basketball strategy
... unsupervised machine learning techniques in clustering to discover new player categorizations and apply neural networks to create improved models of effective field goal percentage and effective shot ...
74
Machine learning for IoT network monitoring
... context, Machine Learning techniques can be leveraged to detect attacks in IoT ...analysis techniques well suited to detect a deviation from the expected ...present machine ...
4
Classifying superheavy elements by machine learning
... cutting-edge machine learning techniques, we find the relationship between atomic data and classification of elements, and further identify that Mt, Ds, Mc, Rg, Lv, Ts, and Og should be metals, while ...
8
Scikit-learn: Machine Learning in Python
... of machine learning algorithms, both supervised and unsupervised, using a consistent, task-oriented interface, thus enabling easy comparison of methods for a given ...online learning, to scale to ...
7
Graphs in machine learning: an introduction
... in machine learning targets the case of a single graph, numerous applications lead naturally to data sets made of graphs, that is situations in which each data point is a graph (or consists in several ...
13
Transductive learning for statistical machine translation
... performance. We will show how such corpora can be used to achieve higher translation quality. Even if large amounts of bilingual text are given, the training of the statistical models usually suffers from sparse data. ...
10
Interprétabilité en Machine Learning, revue de littérature et perspectives
... en Machine Learning, revue de littérature et perspectives Jean-Marie John-Mathews, LITEM, Université Paris-Saclay Les algorithmes d’apprentissage automatique, et particulièrement les réseaux de neurones ...
5
Early WCET Prediction using Machine Learning
... the machine learning process (first step of Figure ...the machine learning tool that will output a formula predicting the WCET estimate in function of the event ...
10
Structural health monitoring by combining machine learning and dimensionality reduction techniques
... combining machine learning and dimensionality reduction techniques - Revista Internacional de Metodos Numericos para Calculo y Diseno en Ingenieria - ...
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