Results 141 to 150 of about 6,929,542 (320)

Hyperparameter Optimization for Software Bug Prediction Using Ensemble Learning

IEEE Access
Software Bug Prediction (SBP) is an integral process to the software’s success that involves predicting software bugs before their occurrence. Detecting software bugs early in the development process enhances software quality, performance, and reduces ...
Dimah Al-Fraihat, Yousef Sharrab, Abdel-Rahman Al-Ghuwairi, Hamzeh Alshishani, Abdulmohsen Algarni   +4 more
semanticscholar   +1 more source

Hyperparameter optimization with approximate gradient

CoRR, 2016
Most models in machine learning contain at least one hyperparameter to control for model complexity. Choosing an appropriate set of hyperparameters is both crucial in terms of model accuracy and computationally challenging. In this work we propose an algorithm for the optimization of continuous hyperparameters using inexact gradient information.
openaire   +3 more sources

Mutual Adaptation and Influence: Review of Latent Dynamics Models in Human–Robot Interaction

Advanced Robotics Research, EarlyView.
Robots are becoming better teammates by learning hidden patterns in how people act. This review covers how state‐of‐the‐art approaches leverage these patterns to help robots not only react but also anticipate and guide cooperation. Here, we synthesize a unifying framework for these approaches, classify and review existing works, and highlight key ...
Mason O. Smith, Sunny Amatya, Seyed Yousef Soltanian, Jonathan Bush, Wenlong Zhang   +4 more
wiley   +1 more source

Evaluation of Hyperparameter Optimization Techniques for Traditional Machine Learning Models [PDF]

Jisuanji kexue
Reasonable hyperparameters ensure that machine learning models can adapt to different backgrounds and tasks.In order to avoid the inefficiency caused by manual adjustment of a large number of model hyperparameters and a vast search space,various ...
LI Haixia, SONG Danlei, KONG Jianing, SONG Yafei, CHANG Haiyan
doaj   +1 more source

Some considerations regarding the use of multi-fidelity Kriging in the construction of surrogate models

, 2015
Surrogate models or metamodels are commonly used to exploit expensive computational simulations within a design optimization framework. The application of multi-fidelity surrogate modeling approaches has recently been gaining ground due to the potential ...
Toal, David J.J.
core   +1 more source

DRIVE‐SAFE: Data‐Driven Robustness and Informed Validation for Evolving Specifications via Formal Evaluation

Advanced Robotics Research, EarlyView.
DRIVE‐SAFE evaluates learning‐based, black‐box autonomous driving policies against evolving temporal safety requirements using Signal Temporal Logic robustness metrics. It aggregates distributional robustness measures with domain‐informed weights to guide iterative retraining.
Kristy Sakano, Jianyu An, Dinesh Manocha, Huan Xu   +3 more
wiley   +1 more source

Tinker: Hyperparameter Optimization Tool

, 2018
Machine learning models can learn to recognize subtle patterns in complex data, making them useful in a wide variety of regression and classification tasks.
Price, Kurt
core  

Intelligent Sky Guardians (InSkyGuard): An Aerial Robotic Swarm for Autonomous Detection and Entrapment of Rogue Multirotors

Advanced Robotics Research, EarlyView.
Intelligent Sky Guardians (InSkyGuard) is introduced as a four‐drone swarm that autonomously detects, tracks, and safely captures rogue drones using a coordinated net system. Computer vision and leader–follower control architecture enable synchronized enclosure, while integrated failsafes enhance system reliability. Validated through closed‐environment
Joshua Hastings, Levanya Dewanarayana, Iline Shaju, Haider Al‐Wizni, Sampson Marquis, Weicheng Huang, Pooya Sareh   +6 more
wiley   +1 more source

Effect of hyperparameter tuning of machine learning algorithms on the modeling quality of the distribution of three mosquito species in Morocco

Journal of Intelligent Systems
The widespread use of machine learning algorithms in dataset modeling requires a thorough understanding of the various tools likely to improve the modeling quality.
Douider Meriem, Amrani Ibrahim, Abik Mounia   +2 more
doaj   +1 more source

Learning‐Based Soft Robotic Grasping: Recent Progress and Remaining Challenges

Advanced Robotics Research, EarlyView.
This review analyzes learning‐based soft robotic grasping from a pipeline‐oriented perspective, encompassing soft gripper design, multimodal sensing, and learning‐based planning and control. It surveys key neural network architectures and benchmark datasets and identifies critical challenges such as sim‐to‐real transfer, generalization, and continual ...
Arnab Majumder, Jelizaveta Konstantinova, Graham Deacon, Kaspar Althoefer   +3 more
wiley   +1 more source