Towards Integrative Machine Learning and Knowledge Extraction : BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised Selected Papers /
Autor Corporativo: | |
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Otros Autores: | , , , |
Formato: | eBook |
Lenguaje: | English |
Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2017.
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Edición: | 1st ed. 2017. |
Colección: | Lecture Notes in Artificial Intelligence ;
10344 |
Materias: |
Tabla de Contenidos:
- Towards integrative Machine Learning & Knowledge Extraction
- Machine Learning and Knowledge Extraction in Digital Pathology needs an integrative approach
- Comparison of Public-Domain Software and Services for Probabilistic Record Linkage and Address Standardization
- Better Interpretable Models for Proteomics Data Analysis Using rule-based Mining
- Probabilistic Logic Programming in Action
- Persistent topology for natural data analysis — A survey
- Predictive Models for Differentiation between Normal and Abnormal EEG through Cross-Correlation and Machine Learning Techniques
- A Brief Philosophical Note on Information
- Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
- A Fast Semi-Automatic Segmentation Tool for Processing Brain Tumor Images
- Topological characteristics of oil and gas reservoirs and their applications
- Convolutional and Recurrent Neural Networks for Activity Recognition in Smart Environment.