Results 41 to 50 of about 283,145 (183)

Karush-Kuhn-Tucker conditions and Lagrangian approach for improving machine learning techniques: A survey and new developments

Atti della Accademia Peloritana dei Pericolanti : Classe di Scienze Fisiche, Matematiche e Naturali
In this work we propose new proofs of some classical results of nonlinear programming milestones, in particular for the Kuhn-Tucker conditions and Lagrangian methods and functions.
Tiziana Ciano, Massimiliano Ferrara
doaj   +1 more source

Supervised Transfer Learning for Product Information Question Answering

, 2019
Popular e-commerce websites such as Amazon offer community question answering systems for users to pose product related questions and experienced customers may provide answers voluntarily.
Bui, Trung, Lai, Tuan Manh, Li, Sheng, Lipka, Nedim   +3 more
core   +1 more source

Quadratic hyper-surface kernel-free large margin distribution machine-based regression and its least-square form

Machine Learning: Science and Technology
ε -Support vector regression ( ε -SVR) is a powerful machine learning approach that focuses on minimizing the margin, which represents the tolerance range between predicted and actual values.
Hao He, Kuaini Wang, Yuzhu Jiang, Huimin pei   +3 more
doaj   +1 more source

Review of Machine Learning Approaches to Semantic Web Service Discovery

Journal of Advances in Information Technology, 2010
A Web service can discover and invoke any service anywhere on the Web, independently of the language, location, machine, or other implementation details.
Shalini Batra, Seema Bawa
doaj   +1 more source

Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography

Frontiers in Oncology, 2021
ObjectiveTo build radiomics models using features extracted from DCE-MRI and mammography for diagnosis of breast cancer.Materials and Methods266 patients receiving MRI and mammography, who had well-enhanced lesions on MRI and histologically confirmed ...
You-Fan Zhao, Zhongwei Chen, Yang Zhang, Jiejie Zhou, Jeon-Hor Chen, Jeon-Hor Chen, Kyoung Eun Lee, Freddie J. Combs, Ritesh Parajuli, Rita S. Mehta, Meihao Wang, Min-Ying Su, Min-Ying Su   +12 more
doaj   +1 more source

Machine learning approach to reconstruct density matrices from quantum marginals

Machine Learning: Science and Technology
Abstract In this work, we propose a machine learning (ML)-based approach to address a specific aspect of the Quantum Marginal Problem: reconstructing a global density matrix compatible with a given set of quantum marginals. Our method integrates a quantum marginal imposition technique with convolutional denoising autoencoders.
Daniel Uzcategui-Contreras, Antonio Guerra, Sebastian Niklitschek, Aldo Delgado   +3 more
openaire   +3 more sources

Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules

Scientific Reports
We evaluated the predictive value of radiomics features from different peritumoral ranges for the invasiveness of ground-glass nodular lung adenocarcinoma using various machine learning models.
Xiao Wang, Hui Xue, Wei Ding, Fei Huang, Yu Zhang, Xin Pang   +5 more
doaj   +1 more source

The Marginal Value of Adaptive Gradient Methods in Machine Learning

, 2017
Adaptive optimization methods, which perform local optimization with a metric constructed from the history of iterates, are becoming increasingly popular for training deep neural networks. Examples include AdaGrad, RMSProp, and Adam. We show that for simple overparameterized problems, adaptive methods often find drastically different solutions than ...
Wilson, Ashia C., Roelofs, Rebecca, Stern, Mitchell, Srebro, Nathan, Recht, Benjamin   +4 more
openaire   +2 more sources

Minimum Density Hyperplanes [PDF]

, 2016
Associating distinct groups of objects (clusters) with contiguous regions of high probability density (high-density clusters), is central to many statistical and machine learning approaches to the classification of unlabelled data.
Hofmeyr, David P., Pavlidis, Nicos G., Tasoulis, Sotiris K.   +2 more
core   +2 more sources

Optimization of Short-Term Relaxation Effect by Dual Error Margin Scheme for Resistive Random-Access Memory-Based Next Generation Reservoir Computing

IEEE Journal of the Electron Devices Society
Next Generation Reservoir Computing (NGRC) has demonstrated strong potential in hardware-based machine learning applications, leveraging RRAM for efficient feature vector generation.
Qingyun Zuo, Danian Dong, Xiaoxin Xu, Woyu Zhang, Dashan Shang, Xu Zheng, Wenxuan Sun, Hao Min   +7 more
doaj   +1 more source