Results 91 to 100 of about 1,169,808 (377)

Quantitative Comparison of Tree Ensemble Learning Methods for Perfume Identification Using a Portable Electronic Nose

Applied Sciences, 2022
Perfume identification (PI) based on an electronic nose (EN) can be used for exposing counterfeit perfumes more time-efficiently and cost-effectively than using gas chromatography and mass spectrometry instruments.
Mengli Cao, Xingwei Ling
doaj   +1 more source

Learning Binary Decision Trees by Argmin Differentiation

, 2020
We address the problem of learning binary decision trees that partition data for some downstream task. We propose to learn discrete parameters (i.e., for tree traversals and node pruning) and continuous parameters (i.e., for tree split functions and prediction functions) simultaneously using argmin differentiation. We do so by sparsely relaxing a mixed-
Zantedeschi, Valentina, Kusner, Matt J, Niculae, Vlad   +2 more
openaire   +2 more sources

Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics

Advanced Engineering Materials, EarlyView.
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani, Aditi Singh, Behnam Ashrafi   +2 more
wiley   +1 more source

Research on Customer Churn Prediction Using Machine Learning Models [PDF]

SHS Web of Conferences
The percentage of consumers or subscribers that discontinue using a product or service within a given time frame is known as the “churn rate.” Hence, using machine learning models to estimate the number of possible churn consumers is crucial for ...
Jia Xiaolei
doaj   +1 more source

Learning Nonlinear Functions Using Regularized Greedy Forest

, 2013
We consider the problem of learning a forest of nonlinear decision rules with general loss functions. The standard methods employ boosted decision trees such as Adaboost for exponential loss and Friedman's gradient boosting for general loss.
Johnson, Rie, Zhang, Tong
core   +2 more sources

Static and Dynamic Behavior of Novel Y‐Shaped Sandwich Beams Subjected to Compressive Loadings: Integration of Supervised Learning and Experimentation

Advanced Engineering Materials, EarlyView.
In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi, Saeed Kamarian, Mahdi Bodaghi, Saeed Saber‐Samandari, Jung‐il Song   +4 more
wiley   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source

Forgetting Exceptions is Harmful in Language Learning [PDF]

, 1998
We show that in language learning, contrary to received wisdom, keeping exceptional training instances in memory can be beneficial for generalization accuracy.
Bosch, Antal van den, Daelemans, Walter, Zavrel, Jakub   +2 more
core   +5 more sources

Advancing Wildfire‐Retardant Materials: Engineering Strategies for Direct and Indirect Suppression

Advanced Engineering Materials, EarlyView.
Here, the evolution, ecological impact, and performance of current fire‐retardant materials and suppression strategies are reviewed, offering an engineering perspective to address existing challenges and propose pathways for the development of more effective, scalable, and sustainable solutions to meet the demands of a changing climate. Wildfires cause
Changxin Dong, Samya Sen, Athena Kolli, Stewart G. Wilson, Eric Andrew Appel   +4 more
wiley   +1 more source

Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset

Energies, 2018
Distributed PV power generation necessitates both intra-hour and day-ahead forecasting of solar irradiance. The UTSA SkyImager is an inexpensive all-sky imaging system built using a Raspberry Pi computer with camera.
Ariana Moncada, Walter Richardson, Rolando Vega-Avila   +2 more
doaj   +1 more source