Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal.
Tipo de material:
TextoProductor: Amsterdam : Distribuidor: Elsevier, Fecha de copyright: ©2017Edición: 4a ediciónDescripción: xxxii, 621 páginas : ilustraciones, gráficas ; 24 x 19 cmTipo de contenido: - texto
- sin medio
- volumen
- 9780128042915
- QA 76 .9 .D343 W82 2017
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Biblioteca Antonio Enriquez Savignac | Biblioteca Antonio Enriquez Savignac | COLECCIÓN RESERVA | QA 76 .9 .D343 W82 2017 (Navegar estantería(Abre debajo)) | 1 | No para préstamo | Ingeniería en Datos e Inteligencia Organizacional | 040445 |
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| QA 76 .9 .D343 M46 2014 Big data : a revolution that will transform how we live, work, and think / | QA 76 .9 .D343 R56 2022 Data analysis with Python and PySpark / | QA 76 .9 .D343 R87 2018 Mining the social web : data minig facebook, twitter, linkedin, instagram, github, and more / | QA 76 .9 .D343 W82 2017 Data mining : practical machine learning tools and techniques / | QA76.9.D35 C1368 2006 Estructuras de datos / | QA 76 .9 .D35 S25 1990 Applications of spatial data structures : computer graphics, image processing, and gis / | QA 76 .9 .D35 S27 Foundations of multidimensional and metric data structures / |
Incluye referencias bibliográficas: páginas 573-600
Part I: Introduction to data mining -- Chapter 1. What's it all about? -- Chapter 2. Input: Concepts, instances, attributes -- Chapter 3. Output: Knowledge representation -- Chapter 4. Algorithms: The basic methods -- Chapter 5. Credibility: Evaluating what's been learned -- Part II: More advanced machine learning schemes -- Chapter 6. Trees and rules -- Chapter 7. Extending instance-based and linear models -- Chapter 8. Data transformations -- Chapter 9. Probabilistic methods -- Chapter 10. Deep learning -- Chapter 11. Beyond supervised and unsupervised learning -- Chapter 12. Ensemble learning -- Chapter 13. Moving on: applications and beyond
"Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research." -- P. web editorial
Ingeniería de Datos e Intelegiencia
NUEVOSDATOS
