Lista de conjuntos de datos para la investigación del aprendizaje automático

Machine learning based fault detection in Electronics Circuit

Estos conjuntos de datos se utilizan en la investigación del aprendizaje automático (ML) y han sido citados en revistas académicas revisadas por pares . Los conjuntos de datos son una parte integral del campo del aprendizaje automático. Los avances importantes en este campo pueden resultar de los avances en los algoritmos de aprendizaje (como el aprendizaje profundo ), el hardware de las computadoras y, de manera menos intuitiva, la disponibilidad de conjuntos de datos de entrenamiento de alta calidad. ^[1] Los conjuntos de datos de entrenamiento etiquetados de alta calidad para algoritmos de aprendizaje automático supervisados ​​y semisupervisados ​​suelen ser difíciles y costosos de producir debido a la gran cantidad de tiempo necesario para etiquetar los datos. Aunque no necesitan estar etiquetados, los conjuntos de datos de alta calidad para el aprendizaje no supervisado también pueden ser difíciles y costosos de producir. ^{[2] [3] [4]}

Muchas organizaciones, incluidos los gobiernos, publican y comparten sus conjuntos de datos . Los conjuntos de datos se clasifican, según las licencias, como datos abiertos y datos no abiertos .

Los conjuntos de datos de varios organismos gubernamentales se presentan en la Lista de sitios de datos gubernamentales abiertos . Los conjuntos de datos se trasladan a portales de datos abiertos . Están disponibles para búsqueda, depósito y acceso a través de interfaces como Open API . Los conjuntos de datos están disponibles como varios tipos y subtipos ordenados.

Lista de ordenamientos utilizados para conjuntos de datos

Los portales de datos se clasifican según su tipo de licencia. Los portales de datos basados ​​en licencias de código abierto se conocen como portales de datos abiertos y son utilizados por muchas organizaciones gubernamentales e instituciones académicas .

Lista de portales de datos abiertos

Lista de portales adecuados para múltiples tipos de aplicaciones

El portal de datos a veces enumera una amplia variedad de subtipos de conjuntos de datos pertenecientes a muchas aplicaciones de aprendizaje automático .

Lista de portales adecuados para un subtipo específico de aplicaciones

Los portales de datos que son adecuados para un subtipo específico de aplicación de aprendizaje automático se enumeran en las secciones siguientes.

Datos de imagen

Datos de texto

Estos conjuntos de datos consisten principalmente en texto para tareas como procesamiento del lenguaje natural , análisis de sentimientos , traducción y análisis de clústeres .

Reseñas

Artículos de noticias

Mensajes

Twitter y tweets

Diálogos

Legal

Otro texto

Datos de sonido

Estos conjuntos de datos consisten en sonidos y características de sonido que se utilizan para tareas como el reconocimiento de voz y la síntesis de voz .

Discurso

Música

Otros sonidos

Datos de señal

Conjuntos de datos que contienen información de señales eléctricas que requieren algún tipo de procesamiento de señales para su posterior análisis.

Eléctrico

Seguimiento de movimiento

Otras señales

Datos físicos

Conjuntos de datos de sistemas físicos.

Física de altas energías

Sistemas

Astronomía

Ciencias de la tierra

Otros físicos

Datos biológicos

Conjuntos de datos de sistemas biológicos.

Humano

Animal

Hongos

Planta

Microbio

Descubrimiento de fármacos

Datos anómalos

Datos de preguntas y respuestas

Esta sección incluye conjuntos de datos que tratan datos estructurados.

Datos solicitados mediante diálogo o instrucción

Esta sección incluye conjuntos de datos que...

Ciberseguridad

Clima y sostenibilidad

Datos del código

Multivariate data

Financial

Weather

Census

Transit

Internet

Games

Other multivariate

Curated repositories of datasets

As datasets come in myriad formats and can sometimes be difficult to use, there has been considerable work put into curating and standardizing the format of datasets to make them easier to use for machine learning research.

• OpenML:^[487] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms.
• PMLB:^[488] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms. Provides classification and regression datasets in a standardized format that are accessible through a Python API.
• Metatext NLP: https://metatext.io/datasets web repository maintained by community, containing nearly 1000 benchmark datasets, and counting. Provides many tasks from classification to QA, and various languages from English, Portuguese to Arabic.
• Appen: Off The Shelf and Open Source Datasets hosted and maintained by the company. These biological, image, physical, question answering, signal, sound, text, and video resources number over 250 and can be applied to over 25 different use cases.^[489][490]

See also

• Comparison of deep learning software
• List of manual image annotation tools
• List of biological databases

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