| Revista: | Polibits |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000376474 |
| ISSN: | 1870-9044 |
| Autores: | Schnitzer, Steffen1 Schmidt, Sebastian1 Rensing, Christoph1 Harriehausen Miihlbauer, Bettina2 |
| Instituciones: | 1Technische Universitat Darmstadt, Multimedia Communications Laboratory, Darmstadt, Hessen. Alemania 2University of Applied Sciences, Darmstadt, Hessen. Alemania |
| Año: | 2014 |
| Periodo: | Ene-Jun |
| Número: | 49 |
| Paginación: | 39-46 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Analítico, descriptivo |
| Resumen en inglés | Classification of text remains a challenge. Most machine learning based approaches require many manually annotated training instances for a reasonable accuracy. In this article we present an approach that minimizes the human annotation effort by interactively incorporating human annotators into the training process via active learning of an ensemble learner. By passing only ambiguous instances to the human annotators the effort is reduced while maintaining a very good accuracy. Since the feedback is only used to train an additional classifier and not for re-training the whole ensemble, the computational complexity is kept relatively low |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial, Aprendizaje de máquinas, Clasificación de textos, Aprendizaje activo |
| Keyword: | Computer science, Artificial intelligence, Machine learning, Text classification, Active learning |
| Texto completo: | Texto completo (Ver HTML) |
