Results 31 to 40 of about 93,556 (252)
Sherpa: Robust hyperparameter optimization for machine learning
SoftwareX, 2020 Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function evaluations, such as the hyperparameter tuning of deep neural networks.
Lars Hertel +4 more
doaj +1 more source
Effect of Hyperparameter Tuning on Performance on Classification model
International Journal of Applied Sciences and Smart TechnologiesThis research aims to analyze the effect of hyperparameter tuning on the performance of Logistic Regression, K-Nearest Neighbours, Support Vector Machine, Decision Tree, Random Forest, Random Forest Classifier, Naive Bayes algorithms.
Muhammad Sholeh +2 more
doaj +1 more source
Use of Augmentation Data and Hyperparameter Tuning in Batik Type Classification using the CNN Model
Sistemasi: Jurnal Sistem InformasiBatik is one of Indonesia's most recognized artistic cultures in the world and has different motifs and types of traditional batik and each has its own uniqueness.
Siti Auliaddina, Toni Arifin
doaj +1 more source
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
A Comparative Study on Regularization Strategies for Embedding-based Neural Networks
, 2015 This paper aims to compare different regularization strategies to address a common phenomenon, severe overfitting, in embedding-based neural networks for NLP. We chose two widely studied neural models and tasks as our testbed. We tried several frequently
Chen, Yunchuan +5 more
core +1 more source
Comparative additive manufacturing defect prediction accuracy with a few transfer learning implementations of deep learning models [PDF]
EPJ Web of ConferencesThis paper addresses the problem of a comprehensive quality assurance strategy for additively manufactured components with integrated in-situ inspection and artificial intelligence and machine learning (AIML) models.
Bajpai Anamol +3 more
doaj +1 more source
Is One Hyperparameter Optimizer Enough?
, 2018 Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best ...
Bergstra J. +3 more
core +1 more source
Artificial Intelligence in GeosciencesMachine learning (ML) algorithms are frequently used in landslide susceptibility modeling. Different data handling strategies may generate variations in landslide susceptibility modeling, even when using the same ML algorithm.
Guruh Samodra +2 more
doaj +1 more source
Tumor mutational burden as a determinant of metastatic dissemination patterns
Molecular Oncology, EarlyView.This study performed a comprehensive analysis of genomic data to elucidate whether metastasis in certain organs share genetic characteristics regardless of cancer type. No robust mutational patterns were identified across different metastatic locations and cancer types.
Eduardo Candeal +4 more
wiley +1 more source
FEBS Open Bio, EarlyView.In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes +32 more
wiley +1 more source