Search Results - "Biehl, Michael"

Leveraging ordinal generalized matrix learning vector quantization for improved classification.

Authors: Abdi, Lida Prete, Alessandro Arlt, Wiebke et al.

Source: Neural Computing & Applications; Mar2026, Vol. 38 Issue 6, p1-18, 18p

Forecasting relative returns for S&P 500 stocks using machine learning.

Authors: Htun, Htet Htet Biehl, Michael Petkov, Nicolai

Source: Financial Innovation. 4/20/2024, Vol. 10, p1-16. 16p.

The Study Process of the IMC Centenary.

Authors: Biehl, Michael, michael.biehl@mission‐weltweit.de

Source: International Review of Mission; Nov2022, Vol. 111 Issue 2, p352-359, 8p

MULTICULTURALISM.

Authors: Biehl, Michael

Source: Blue Unicorn; Fall2025, Vol. 49 Issue 1, p36-36, 1p

Survey of feature selection and extraction techniques for stock market prediction.

Authors: Htun, Htet Htet Biehl, Michael Petkov, Nicolai

Source: Financial Innovation. 1/12/2023, Vol. 9 Issue 1, p1-25. 25p.

Trajectories of Depressed Mood from Early Adolescence to Young Adulthood: The Effects of Pubertal Timing and Adolescent Dating

Authors: Natsuaki, Misaki N. Biehl, Michael C. Ge, Xiaojia

Source: Journal of Research on Adolescence. Mar 2009 19(1):47-74.

Peer Reviewed: Y

Page Count: 28

Descriptors: Early Adolescents, Adolescents, Puberty, Gender Differences, Depression (Psychology), Adolescent Development, Dating (Social), Maturity (Individuals), Age Differences, Interpersonal Competence, Emotional Development, Social Development, Psychological Patterns

Adaptive basis functions for prototype-based classification of functional data.

Authors: Melchert, Friedrich Bani, Gabriele Seiffert, Udo et al.

Source: Neural Computing & Applications; 2020, Vol. 32 Issue 24, p18213-18223, 11p

Reconciliation as Expression of Christ's Love: Reflections on the Next WCC Assembly Theme.

Authors: Biehl, Michael

Source: International Review of Mission; Nov2020, Vol. 109 Issue 2, p151-162, 12p

FA(IR)2MA-GLVQ – A hidden-feature-bias mitigation approach for fairness in classification learning based on generalized matrix learning vector quantization.

Authors: Kaden, Marika Schubert, Ronny Voigt, Julius et al.

Source: Neurocomputing; May2026, Vol. 678, pN.PAG-N.PAG, 1p

Views on workplace culture and climate: through the lens of retention and Title IV-E participation.

Authors: Rao Hermon, Sandhya Biehl, Michael Chahla, Rose

Source: Journal of Public Child Welfare. Jul/Aug2018, Vol. 12 Issue 3, p380-397. 18p. 3 Charts, 1 Graph.

Iterated relevance matrix analysis for improved classification and robustness in prototype-based learning schemes.

Authors: Lövdal, Sofie van den Brandhof, Elina L. Biehl, Michael

Source: Neurocomputing; Mar2026, Vol. 670, pN.PAG-N.PAG, 1p

Performance analysis of LVQ algorithms: A statistical physics approach.

Authors: Ghosh, Anarta Biehl, Michael Hammer, Barbara

Source: Neural Networks; July/August 2006, Vol. 19 Issue 6/7, p817-829, 13p

Prototype-based Models for the Supervised Learning of Classification Schemes.

Authors: Biehl, Michael Hammer, Barbara Villmann, Thomas et al.

Source: Proceedings of the International Astronomical Union. Oct2016 Supplement, Vol. 12 Issue S325, p129-138. 10p.

Hermite polynomials facilitating on-line learning analysis of layered neural networks with arbitrary activation function.

Authors: Citton, Otavio Richert, Frederieke Biehl, Michael et al.

Source: Neurocomputing; Nov2025, Vol. 655, pN.PAG-N.PAG, 1p

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Book Review: A Short History of Christianity beyond the West. Asia, Africa, Latin America 1450-2000 by Koschorke Klaus.

Authors: Biehl, Michael¹

Source: Transformation (02653788); Oct2025, Vol. 42 Issue 4, p348-350, 3p

La Négresse.

Authors: BIEHL, MICHAEL

Source: Cimarron Review; 2017, Issue 198, p11-21, 11p

Letters to the Editor.

Authors: Reed, Dian Duchin Biehl, Michael Dupuis, Kelley

Source: Poetry; January 2006, Vol. 187 Issue 4, p343-345, 3p

sklvq: Scikit Learning Vector Quantization.

Authors: van Veen, Rick Biehl, Michael de Vries, Gert-Jan

Source: Journal of Machine Learning Research. 2021, Vol. 22, p1-6. 6p.

Subjects: Vector quantization, Python programming language, Modular design

Analysis of Flow Cytometry Data by Matrix Relevance Learning Vector Quantization.

Authors: Biehl, Michael Bunte, Kerstin Schneider, Petra

Source: PLoS ONE. Mar2013, Vol. 8 Issue 3, p1-11. 11p.