Results 81 to 90 of about 2,475,679 (305)

Evaluating Machine Learning Approaches in Structural Equation Modelling to Improve Predictive Accuracy in Marketing Research

Indonesian Journal of Business and Entrepreneurship
Background: This study aimed to fill a critical research gap by comparing traditional Structural Equation Modelling (SEM) with hybrid Bayesian-Machine Learning (ML) models in marketing research, focusing on the limited exploration of these advanced ...
Chacha Magasi
doaj   +1 more source

Hierarchic Bayesian models for kernel learning [PDF]

Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005
The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance when compared to that obtained from any single data source. We present a Bayesian hierarchical model which enables kernel learning and present effective variational Bayes ...
Girolami, M., Rogers, S.
openaire   +1 more source

Isolation Defines Identity: Functional Consequences of Extracellular Vesicle Purification Strategies

Advanced Healthcare Materials, EarlyView.
Four extracellular vesicle purification strategies are compared using ovarian‐cancer ascites and ES‐2 cell supernatants. A novel workflow links purification to function by combining particle‐normalized proteomics with matched cell‐free and cell‐based assays.
Christian Preußer, Dolores J. Salander, Witold Szymanski, Johannes Graumann, Yukai Wang, Kai Zhao, Jörg W. Bartsch, María Gómez‐Serrano, Daniel Bachurski, Silke Reinartz, Elke von Pogge von Strandmann   +10 more
wiley   +1 more source

Bayesian Test of Significance for Conditional Independence: The Multinomial Model

Entropy, 2014
Conditional independence tests have received special attention lately in machine learning and computational intelligence related literature as an important indicator of the relationship among the variables used by their models.
Pablo de Morais Andrade, Julio Michael Stern, Carlos Alberto de Bragança Pereira   +2 more
doaj   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Integration of Bayesian Methods in Machine Learning: A Theoretical and Empirical Review

INSERT
Abstrak Studi ini merupakan sebuah tinjauan literatur sistematis yang mendalami integrasi metode Bayesian dalam pembelajaran mesin. Metode Bayesian telah terbukti memberikan keuntungan signifikan dalam menangani ketidakpastian dan variabilitas data ...
Syaharuddin Syaharuddin
doaj   +1 more source

Weight Priors for Learning Identity Relations [PDF]

, 2019
Learning abstract and systematic relations has been an open issue in neural network learning for over 30 years. It has been shown recently that neural networks do not learn relations based on identity and are unable to generalize well to unseen data. The
Kopparti, R. M., Weyde, T.
core  

Occam factors and model-independent Bayesian learning of continuous distributions

, 2002
Learning of a smooth but nonparametric probability density can be regularized using methods of Quantum Field Theory. We implement a field theoretic prior numerically, test its efficacy, and show that the data and the phase space factors arising from the ...
Bialek, William, Nemenman, Ilya
core   +1 more source

Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics

Advanced Materials, EarlyView.
An active learning framework is introduced for the accelerated discovery of high‐entropy chalcogenides with superior thermoelectric performance. Only 80 targeted syntheses, selected from 16206 possible combinations, led to three high‐performance compositions, demonstrating the remarkable efficiency of data‐driven guidance in experimental materials ...
Hanhwi Jang, Wooseok Lee, Hwa‐Jung Kim, Sohyang Cha, Hosun Shin, Won Bo Lee, Min‐Wook Oh, Yeon Sik Jung, YongJoo Kim   +8 more
wiley   +1 more source

Navigating Ternary Doping in Li‐ion Cathodes With Closed‐Loop Multi‐Objective Bayesian Optimization

Advanced Materials, EarlyView.
The search for advanced battery materials is pushing us into highly complex composition spaces. Here, a space with about 14 million unique combinations is efficiently explored using high‐throughput experimentation guided by Bayesian optimization with a deep kernel trained on both the Materials Project database and our data.
Nooshin Zeinali Galabi, Cheng‐Hao Liu, Moksh Jain, Marc Kamel, Shipeng Jia, Yoshua Bengio, Eric McCalla   +6 more
wiley   +1 more source