A Feature-Centric View of Information RetrievalModel Learning
A Feature-Centric View of Information Retrieval: Model Learning
Metzler, Donald
2011-07-14 00:00:00
[This chapter describes how the parameters and features of feature-based ranking functions can be learned from training data. The discussion begins by describing a number of techniques to estimate the model parameters in such a way that the resulting ranking functions are optimized for a target retrieval metric. The section describes a number of numerical analysis-based approaches, as well as a number of more sophisticated machine learning-inspired approaches, which are often referred to as learning to rank approaches. The chapter then continues by describing effective feature selection strategies for feature-based ranking functions and concludes by describing a new line of research that aims at learning models that are both highly effective, but also very efficient.]
http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.pnghttp://www.deepdyve.com/lp/springer-journals/a-feature-centric-view-of-information-retrieval-model-learning-xQ4x1gOJJI
A Feature-Centric View of Information RetrievalModel Learning
[This chapter describes how the parameters and features of feature-based ranking functions can be learned from training data. The discussion begins by describing a number of techniques to estimate the model parameters in such a way that the resulting ranking functions are optimized for a target retrieval metric. The section describes a number of numerical analysis-based approaches, as well as a number of more sophisticated machine learning-inspired approaches, which are often referred to as learning to rank approaches. The chapter then continues by describing effective feature selection strategies for feature-based ranking functions and concludes by describing a new line of research that aims at learning models that are both highly effective, but also very efficient.]
Published: Jul 14, 2011
Keywords: Information Retrieval; Average Precision; Ranking Function; Retrieval Model; Mean Average Precision
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