Advances in Data Science

Chapter 1: Randomized Iterative Methods for Tensor Regression Under the t-product.- Chapter 2: Matrix exponentials: Lie-Trotter-Suzuki fractal decomposition, Gauss Runge-Kutta polynomial formulation, and compressible features.- Chapter 3: An exploration of graph distances, graph curvature, and applications to network analysis.- Chapter 4: Time-Varying Graph Signal Recovery Using High-Order Smoothness and Adaptive Low-rankness.- Chapter 5: Graph-Directed Topic Models of Text Documents.- Chapter 6: Linear independent component analysis in Wasserstein space.- Chapter 7: Faster Hodgerank Approximation Algorithm for Statistical Ranking and User Recommendation Problems.- Chapter 8: A Comparison Study of Graph Laplacian Computation.- Chapter 9: Supervised Dimension Reduction via Local Gradient Elongation.- Chapter 10: Reducing NLP Model Embeddings for Deployment in Embedded Systems.- Chapter 11: Automated extraction of roadside slope from aerial LiDAR data in rural North Carolina.- Chapter 12: A non-parametric optimal design algorithm for population pharmacokinetics.- Chapter 13: Unrolling Deep Learning End-to-End Method for Phase Retrieval.- Chapter 14: Performance Analysis of MFCC and wav2vec on Stuttering Data.- Chapter 15: Active Learning for Reducing Gender Gaps in Undergraduate Computing and Data Science.- Chapter 16: Quantifying and Documenting Gender-Based Inequalities in the Mathematical Sciences in the United States.

• Erscheinungsdatum: 26.09.2025
• Autor/Autorin: Cristina Garcia-Cardona
• Reihe: Mathematics and Statistics (R0)
• Format: E-Book
• Dateiformat: PDF
• Kopierschutz: Wasserzeichen
• Dateigröße: 23.2 MB
• Verlag: SPRINGER
• Sprache: Englisch
• Umfang: 357 Seiten
• ISBN: 9783031878046
• Lieferung: Sofort per Download
• Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
• Kompatibilität: Lesbar auf Geräten und Apps mit PDF-Unterstützung.