Andere Kunden interessierten sich auch für
![Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks]( "Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks")
Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks37,99 €![Hierarchical Feature Selection for Knowledge Discovery]( "Hierarchical Feature Selection for Knowledge Discovery")
Hierarchical Feature Selection for Knowledge Discovery74,99 €![Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVII]( "Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVII")
Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVII37,99 €![Transactions on Large-Scale Data- and Knowledge-Centered Systems XLI]( "Transactions on Large-Scale Data- and Knowledge-Centered Systems XLI")
Transactions on Large-Scale Data- and Knowledge-Centered Systems XLI37,99 €![Data Science Thinking]( "Data Science Thinking")
Data Science Thinking55,99 €![Data-Driven Prediction for Industrial Processes and Their Applications]( "Data-Driven Prediction for Industrial Processes and Their Applications")
Data-Driven Prediction for Industrial Processes and Their Applications95,99 €![Learning Representation for Multi-View Data Analysis]( "Learning Representation for Multi-View Data Analysis")
Learning Representation for Multi-View Data Analysis95,99 €
This SpringerBrief covers the technical material related to large scale hierarchical classification (LSHC). HC is an important machine learning problem that has been researched and explored extensively in the past few years. In this book, the authors provide a comprehensive overview of various state-of-the-art existing methods and algorithms that were developed to solve the HC problem in large scale domains. Several challenges faced by LSHC is discussed in detail such as:
1. High imbalance between classes at different levels of the hierarchy
2. Incorporating relationships during model learning leads to optimization issues
3. Feature selection
4. Scalability due to large number of examples, features and classes
5. Hierarchical inconsistencies
6. Error propagation due to multiple decisions involved in making predictions for top-down methods
The brief also demonstrates how multiple hierarchies can be leveraged forimproving the HC performance using different Multi-Task Learning (MTL) frameworks.
The purpose of this book is two-fold:
1. Help novice researchers/beginners to get up to speed by providing a comprehensive overview of several existing techniques.
2. Provide several research directions that have not yet been explored extensively to advance the research boundaries in HC.
New approaches discussed in this book include detailed information corresponding to the hierarchical inconsistencies, multi-task learning and feature selection for HC. Its results are highly competitive with the state-of-the-art approaches in the literature.
1. High imbalance between classes at different levels of the hierarchy
2. Incorporating relationships during model learning leads to optimization issues
3. Feature selection
4. Scalability due to large number of examples, features and classes
5. Hierarchical inconsistencies
6. Error propagation due to multiple decisions involved in making predictions for top-down methods
The brief also demonstrates how multiple hierarchies can be leveraged forimproving the HC performance using different Multi-Task Learning (MTL) frameworks.
The purpose of this book is two-fold:
1. Help novice researchers/beginners to get up to speed by providing a comprehensive overview of several existing techniques.
2. Provide several research directions that have not yet been explored extensively to advance the research boundaries in HC.
New approaches discussed in this book include detailed information corresponding to the hierarchical inconsistencies, multi-task learning and feature selection for HC. Its results are highly competitive with the state-of-the-art approaches in the literature.