—One common approach to active learning is to iteratively train a single classifier by choosing data points based on its uncertainty, but it is nontrivial to design uncertainty ...
Data mining techniques have become central to many applications. Most of those applications rely on so called supervised learning algorithms, which learn from given examples in th...
Anomaly detection for network intrusion detection is usually considered an unsupervised task. Prominent techniques, such as one-class support vector machines, learn a hypersphere ...
In active learning based music retrieval systems, providing multiple samples to the user for feedback is very necessary. In this paper, we present a new multi-samples selection st...
We present an active learning framework to simultaneously learn appearance and contextual models for scene understanding tasks (multi-class classification). Existing multi-class a...
Most of the existing active learning algorithms are based on the realizability assumption: The learner’s hypothesis class is assumed to contain a target function that perfectly c...
Abstract. We analyze the expected cost of a greedy active learning algorithm. Our analysis extends previous work to a more general setting in which different queries have differe...
Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$...
In active learning, one attempts to maximize classifier performance for a given number of labeled training points by allowing the active learning algorithm to choose which points...
In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class. Such applications include spam filt...