Results 61 to 70 of about 853,208 (277)
Interpreting uninterpretable predictors: kernel methods, Shtarkov solutions, and random forests
Statistical Theory and Related Fields, 2022 Many of the best predictors for complex problems are typically regarded as hard to interpret physically. These include kernel methods, Shtarkov solutions, and random forests.
T. M. Le, Bertrand Clarke
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Advanced Engineering Materials, EarlyView.Creep experiments at 900°C on coarse‐grained steel‐ceramic composites containing recycled magnesia reveal that higher ceramic volume fractions significantly enhance the creep resistance. Detailed EBSD investigations identify subgrain formation in the steel matrix as the dominant deformation mechanism.
Moritz Müller +6 more
wiley +1 more source
A Fast Neural Network Learning Algorithm with Approximate Singular Value Decomposition
International Journal of Applied Mathematics and Computer Science, 2019 The learning of neural networks is becoming more and more important. Researchers have constructed dozens of learning algorithms, but it is still necessary to develop faster, more flexible, or more accurate learning algorithms.
Jankowski Norbert, Linowiecki Rafał
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Supervised Kernel Principal Component Analysis by Most Expressive Feature Reordering
Journal of Telecommunications and Information Technology, 2015 The presented paper is concerned with feature space derivation through feature selection. The selection is performed on results of kernel Principal Component Analysis (kPCA) of input data samples.
Krzysztof Ślot +3 more
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Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
wiley +1 more source
Exact heat kernel on a hypersphere and its applications in kernel SVM
, 2017 Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector machine compared to ...
Song, Jun S., Zhao, Chenchao
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Advanced Engineering Materials, EarlyView.This study demonstrates how optimizing laser power, scanning speed, and hatching distance in laser powder bed fusion can boost the productivity of Inconel 718 manufacturing by up to 29% while maintaining mechanical integrity. The work delivers a validated process window and cost–time analysis, offering industry‐ready guidelines for efficient additive ...
Amir Behjat +7 more
wiley +1 more source
Approximate‐Guided Representation Learning in Vision Transformer
CAAI Transactions on Intelligence TechnologyIn recent years, the transformer model has demonstrated excellent performance in computer vision (CV) applications. The key lies in its guided representation attention mechanism, which uses dot‐product to depict complex feature relationships, and ...
Kaili Wang +4 more
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Positive Definite Kernels in Machine Learning [PDF]
, 2009 This survey is an introduction to positive definite kernels and the set of methods they have inspired in the machine learning literature, namely kernel methods.
Cuturi, Marco
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Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source