Results 241 to 250 of about 1,532,491 (274)
Some of the next articles are maybe not open access.
Meta weight learning via model-agnostic meta-learning
Neurocomputing, 2021Abstract While meta learning approaches have achieved remarkable success, obtaining a stable and unbiased meta-learner remains a significant challenge, since the initial model of a meta-learner could be too biased towards existing tasks to adapt to new tasks.
Zhixiong Xu +4 more
openaire +1 more source
Meta-learning in Reinforcement Learning
Neural Networks, 2003Meta-parameters in reinforcement learning should be tuned to the environmental dynamics and the animal performance. Here, we propose a biologically plausible meta-reinforcement learning algorithm for tuning these meta-parameters in a dynamic, adaptive manner.
Nicolas, Schweighofer, Kenji, Doya
openaire +2 more sources
2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS), 2019
Although artificial intelligence and machine learning are currently extremely fashionable, applying machine learning on real-life problems remains very challenging. Data scientists need to evaluate various learning algorithms and tune their numerous parameters, based on their assumptions and experience, against concrete problems and training data sets.
Thomas Hartmann +4 more
openaire +1 more source
Although artificial intelligence and machine learning are currently extremely fashionable, applying machine learning on real-life problems remains very challenging. Data scientists need to evaluate various learning algorithms and tune their numerous parameters, based on their assumptions and experience, against concrete problems and training data sets.
Thomas Hartmann +4 more
openaire +1 more source
2009
The application of Machine Learning (ML) and Data Mining (DM) tools to classification and regression tasks has become a standard, not only in research but also in administrative agencies, commerce and industry (e.g., finance, medicine, engineering).
Christophe Giraud-Carrier +3 more
openaire +1 more source
The application of Machine Learning (ML) and Data Mining (DM) tools to classification and regression tasks has become a standard, not only in research but also in administrative agencies, commerce and industry (e.g., finance, medicine, engineering).
Christophe Giraud-Carrier +3 more
openaire +1 more source
Linking meta-learning to meta-structure
Behavioral and Brain SciencesAbstract We propose that a principled understanding of meta-learning, as aimed for by the authors, benefits from linking the focus on learning with an equally strong focus on structure, which means to address the question: What are the meta-structures that can guide meta-learning?
Schilling, Malte +2 more
openaire +3 more sources
Goal of this work is to make acquaintance and study meta-learningu methods, program algorithm and compare with other machine learning methods.
Hang Wang, Sen Lin, Junshan Zhang
openaire +3 more sources
Hang Wang, Sen Lin, Junshan Zhang
openaire +3 more sources
Algorithm Selection via Meta-Learning and Active Meta-Learning
2019To find most suitable classifier is possible either through cross-validation, which suffers from computational cost or through expert advice which is not always feasible to have. Meta-Learning can be a better approach to automate this process, by generating Meta-Examples which is a combination of performance results of classification algorithms on ...
Nirav Bhatt +3 more
openaire +1 more source
American Cancer Society nutrition and physical activity guideline for cancer survivors
Ca-A Cancer Journal for Clinicians, 2022Cheryl L Rock +2 more
exaly
Artificial Intelligence in Meta-optics
Chemical Reviews, 2022Mu-Ku Chen, Xiaoyuan Liu, Yanni Sun
exaly