The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers,
yet its performance competes with the most complex classifiers in the literature. The core of …

Identifying the queried object, from a large volume of given uncertain dataset, is a tedious
task which involves time complexity and computational complexity. To solve these …

Given two images, we want to predict which exhibits a particular visual attribute more than
the other---even when the two images are quite similar. Existing relative attribute methods …

Learning to rank has been intensively studied and widely applied in information retrieval.
Typically, a global ranking function is learned from a set of labeled data, which can achieve …

Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking
model using training data, such that the model can sort new objects according to their …

This chapter provides an overview of recent work on preference learning and ranking via
pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural …

LETOR is a benchmark collection for the research on learning to rank for information
retrieval, released by Microsoft Research Asia. In this paper, we describe the details of the …

The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers,
yet its performance competes with the most complex classifiers in the literature. The core of …

In machine learning, hyperparameter tuning is the problem of choosing a set of optimal
hyperparameters for a learning algorithm. Several approaches have been widely adopted …

Ranking in information retrieval has been traditionally approached as a pursuit of relevant
information, under the assumption that the users' information needs are unambiguously …