Efficient Margin-Based Rank Learning Algorithms for Information Retrieval
نویسندگان
چکیده
Learning a good ranking function plays a key role for many applications including the task of (multimedia) information retrieval. While there are a few rank learning methods available, most of them need to explicitly model the relations between every pair of relevant and irrelevant documents, and thus result in an expensive training process for large collections. The goal of this paper is to propose a general rank learning framework based on the margin-based risk minimization principle and develop a set of efficient rank learning approaches that can model the ranking relations with much less training time. Its flexibility allows a number of margin-based classifiers to be extended to their rank learning counterparts such as the ranking logistic regression developed in this paper. Experimental results show that this efficient learning algorithm can successfully learn a highly effective retrieval function for multimedia retrieval on the TRECVID’03-’05 collections.
منابع مشابه
ارائه الگوریتمی مبتنی بر یادگیری جمعی به منظور یادگیری رتبهبندی در بازیابی اطلاعات
Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank has been shown to be useful in many applications of information retrieval, natural language processing, and data mining. Learning to rank can be described by two systems: a learning system and a ranking system. The learning system takes training data as input and constructs a ranking ...
متن کاملOnline Learning to Rank for Content-Based Image Retrieval
A major challenge in Content-Based Image Retrieval (CBIR) is to bridge the semantic gap between low-level image contents and high-level semantic concepts. Although researchers have investigated a variety of retrieval techniques using different types of features and distance functions, no single best retrieval solution can fully tackle this challenge. In a real-world CBIR task, it is often highl...
متن کاملPerceptron-like Algorithms and Generalization Bounds for Learning to Rank
Learning to rank is a supervised learning problem where the output space is the space of rankings but the supervision space is the space of relevance scores. We make theoretical contributions to the learning to rank problem both in the online and batch settings. First, we propose a perceptron-like algorithm for learning a ranking function in an online setting. Our algorithm is an extension of t...
متن کاملQuickRank: a C++ Suite of Learning to Rank Algorithms
Ranking is a central task of many Information Retrieval (IR) problems, particularly challenging in the case of large-scale Web collections where it involves effectiveness requirements and efficiency constraints that are not common to other ranking-based applications. This paper describes QuickRank, a C++ suite of efficient and effective Learning to Rank (LtR) algorithms that allows high-quality...
متن کاملAn Effective Approach for Robust Metric Learning in the Presence of Label Noise
Many algorithms in machine learning, pattern recognition, and data mining are based on a similarity/distance measure. For example, the kNN classifier and clustering algorithms such as k-means require a similarity/distance function. Also, in Content-Based Information Retrieval (CBIR) systems, we need to rank the retrieved objects based on the similarity to the query. As generic measures such as ...
متن کامل