Results 91 to 100 of about 10,729 (204)

The fuzzy Kullback-Leibler divergence for estimating parameters of the probability distribution in fuzzy data: an application to classifying Vietnamese Herb Leaves. [PDF]

Sci Rep, 2023
Le H, Sang VNT, Lam Thuy LN, Bao PT.
europepmc   +1 more source

Large‐Scale Structural Dynamics in the Tail Fiber Modulate the Infective Transition of the T7 Bacteriophage

Small, EarlyView.
During host recognition, the fibers of T7 bacteriophages transition from a capsid bound state to an extended conformation, which requires some level of fiber flexibility. By using high‐speed atomic force microscopy, we show that the fibers have an internal molecular hinge and a torsionally compliant coiled‐coil region.
Luca Elizabet Kosik, Miklós Cervenak, Dominik Sziklai, Andrea Balogh‐Molnár, Negar Rahimi, Bence Fehér, Soma Yamamoto, Hiroki Konno, Noriyuki Kodera, Holger Flechsig, Romain Amyot, Heinz Amenitsch, Hedvig Tordai, Levente Herényi, Ana Cuervo, Miklós Kellermayer, Bálint Kiss   +16 more
wiley   +1 more source

Affine Calculus for Constrained Minima of the Kullback–Leibler Divergence

Stats
The non-parametric version of Amari’s dually affine Information Geometry provides a practical calculus to perform computations of interest in statistical machine learning.
Giovanni Pistone
doaj   +1 more source

PowerGPT‐R1: Vision‐Language Reinforcement Fine‐Tuning With Verifiable Reward for Power Inspection

High Voltage, EarlyView.
ABSTRACT Vision‐driven intelligent power inspection systems have long faced the challenge of scarce high‐quality datasets in specialised domains, leading to limited performance of traditional deep learning methods (e.g., Faster‐RCNN) in few‐shot learning scenarios. Recent breakthroughs in language models, particularly the open‐source DeepSeek‐R1 model,
Yangyang Zhong, Pengxin Luo, Yunfeng Yan, Jun Feng, Shixiang Tang, Weizhen He, Yiheng Deng, Yi Chen, Yuhao Zhou, Qi Li, Xianbo Wang, Fushuan Wen, Donglian Qi   +12 more
wiley   +1 more source

Correction : Random-forest model for drug-target interaction prediction via Kullback-Leibler divergence. [PDF]

J Cheminform, 2022
Ahn S, Lee SE, Kim MH.
europepmc   +1 more source

Deep blueprint: A literature review and guide to automated image classification for ecologists

Journal of Animal Ecology, EarlyView.
A practical, literature‐grounded review that gives ecologists a clear, modular workflow for deep learning image classification. With code, GUIs and a novel deep sea case study (automated deep sea biotope classification) it lowers technical barriers and provides a usable blueprint for accelerating, standardising, and scaling ecological image analysis ...
Chloe A. Game, Nils Piechaud, Kerry L. Howell   +2 more
wiley   +1 more source

Using Feature Engineering and Principal Component Analysis for Monitoring Spindle Speed Change Based on Kullback-Leibler Divergence with a Gaussian Mixture Model. [PDF]

Sensors (Basel), 2023
Huang YC, Hou CC.
europepmc   +1 more source

Demand Estimation with Text and Image Data

The RAND Journal of Economics, EarlyView.
ABSTRACT We propose a demand estimation approach that leverages unstructured data to infer substitution patterns. Using pre‐trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a mixed logit demand model.
Giovanni Compiani, Ilya Morozov, Stephan Seiler   +2 more
wiley   +1 more source

Bregman–Hausdorff Divergence: Strengthening the Connections Between Computational Geometry and Machine Learning

Machine Learning and Knowledge Extraction
The purpose of this paper is twofold. On a technical side, we propose an extension of the Hausdorff distance from metric spaces to spaces equipped with asymmetric distance measures.
Tuyen Pham, Hana Dal Poz Kouřimská, Hubert Wagner   +2 more
doaj   +1 more source

Composite marginal likelihood estimation of higher‐order diagnostic classification models under high dimensionality

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract Although full‐information maximum likelihood (FIML) estimation is widely used for diagnostic classification models (DCMs), its computational efficiency deteriorates sharply in high‐dimensional settings. This scalability challenge is increasingly critical as DCMs are applied to large‐scale assessments, psychological testing and longitudinal ...
Minho Lee, Yon Soo Suh
wiley   +1 more source