Results 41 to 50 of about 96,348 (272)

Hyper-parameter Tuning for Quantum Support Vector Machine

Advances in Electrical and Computer Engineering, 2022
In recent years, the positive effect of quantum techniques on machine learning methods have been studied. Especially in training big data, quantum computing is beneficial in terms of speed.
DEMIRTAS, F., TANYILDIZI, E.
doaj   +1 more source

Lipschitz Adaptivity with Multiple Learning Rates in Online Learning [PDF]

, 2019
We aim to design adaptive online learning algorithms that take advantage of any special structure that might be present in the learning task at hand, with as little manual tuning by the user as possible. A fundamental obstacle that comes up in the design
Koolen, Wouter M., Mhammedi, Zakaria, van Erven, Tim   +2 more
core   +1 more source

Predicting Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex Using a Fusion Model Integrating Lesion Network Mapping and Machine Learning

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers.
Tinghong Liu, Qi Wang, Suhui Kuang, Dezhi Cao, Ping Ding, Shaohui Zhang, Haihua Wei, Zhirong Wei, Jinshan Xu, Xinyu Huang, Bing Liu, Shuli Liang   +11 more
wiley   +1 more source

OBOE: Collaborative Filtering for AutoML Model Selection

, 2019
Algorithm selection and hyperparameter tuning remain two of the most challenging tasks in machine learning. Automated machine learning (AutoML) seeks to automate these tasks to enable widespread use of machine learning by non-experts.
Akimoto, Yuji, Kim, Dae Won, Udell, Madeleine, Yang, Chengrun   +3 more
core   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Research and Analysis of IndoBERT Hyperparameter Tuning in Fake News Detection

Jurnal Nasional Teknik Elektro dan Teknologi Informasi
The rapid advancement of communication technology has transformed how information is shared, but it has also brought concerns about the proliferation of false information.
Anugerah Simanjuntak, Rosni Lumbantoruan, Kartika Sianipar, Rut Gultom, Mario Simaremare, Samuel Situmeang, Erwin Panggabean   +6 more
doaj   +1 more source

Hyperparameter Tuning with Renyi Differential Privacy

, 2021
For many differentially private algorithms, such as the prominent noisy stochastic gradient descent (DP-SGD), the analysis needed to bound the privacy leakage of a single training run is well understood. However, few studies have reasoned about the privacy leakage resulting from the multiple training runs needed to fine tune the value of the training ...
Papernot, Nicolas, Steinke, Thomas
openaire   +2 more sources

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Hyperparameter Tuning

This file contains hyperparameter tuning experiments.
  +4 more sources

Evaluasi Performa XGBoost dengan Oversampling dan Hyperparameter Tuning untuk Prediksi Alzheimer

Techno.Com
Alzheimer adalah gangguan neurodegeneratif yang mempengaruhi kemampuan kognitif dan memori, deteksi dini sangat penting untuk pengobatan yang tepat. Namun, untuk mendeteksi Alzheimer memerlukan biaya yang tinggi, sehingga penggunaan machine learning bisa
Furqon Nurbaril Yahya, Mochammad Anshori, Ahsanun Naseh Khudori   +2 more
doaj   +1 more source