SupportVectorMachine

Name: SupportVectorMachine
Creator: Mallik, Neeratyoy
Published: 2022-05-30T00:00:00+08:00

Figshare2022-05-30 更新2026-04-08 收录

参数优化

支持向量机参数优化

数据链接：

https://figshare.com/articles/dataset/SupportVectorMachine/15098280/4 数据链接链接失效反馈

官方服务：

资源简介：

Multi-fidelity tabular data for Support Vector Machine hyperparameter space for HPOBench

提供机构：

Mallik, Neeratyoy

创建时间：

2022-05-30

Metaheuristic parameter tuning dateset

优化算法

参数优化

All thee metaheuristic algorithms (Ant Colony Optimization, Evolutionary Strategy, Imperialist Competitive Algorithm) contain multiple numerical parameters, that can be tuned to increase the efficienc

DataCite Commons2025-06-01 更新150

TWM Data Files

知识图谱嵌入

参数优化

Data files containing the full outputs of all hyperparameter searches needed for TWM to simulate the KGE model ComplEx on 5 KGs -- CoDExSmall, DBpedia50 OpenEA, Kinships, and UMLS. Data is in a pre-pr

NIAID Data Ecosystem80

Pixel intensity statistics after running background subtraction algorithm with variable window sizes.

背景减法算法

参数优化

The window size in FRET-IBRA defines the number of tiles the image width should be divided into, i.e., for a 640x480 pixel image, a window size set at 40 results in 40 windows along the width and 30 w

NIAID Data Ecosystem40

ibm-research/rag-hpo-bench

检索增强生成

参数优化

该数据集名为RAG‑HPO Bench，包含了一篇关于检索增强生成（RAG）超参数优化方法分析的论文中的网格搜索结果。数据集涵盖了162种RAG配置在五个RAG QA数据集（AIArxiv、BioASQ、ClapNQ、MiniWiki和WatsonxQA）上的开发和测试分割的输出和评分。数据集还包括了不同配置的详细结果文件，探索的超参数空间（如块大小、块重叠、嵌入模型、Top-k和生成模型），以及

Hugging Face2026-01-19 更新120

Fractional Cross-Validation for Optimizing Hyperparameters of Supervised Learning Algorithms

机器学习

参数优化

K-fold cross-validation (CV) is a robust method for estimating generalization performance of supervised learning models. Although CV is more reliable than using a single hold-out test set, it is also

DataCite Commons2025-07-29 更新70

SupportVectorMachine

资源简介：

相关数据集