This paper explores a combination of machine learning, approximate text segmentation and a vector-space model to distinguish novel information from repeated information. In experiments with the data from the Novelty Track at the Text Retrieval Conference, we show improvements over a variety of approaches, in particular in raising precision scores on this data, while maintaining a reasonable amo...

The novelty track was first introduced in TREC 2002. Given a TREC topic, the goal of this task in 2004 is to locate relevant and new information from a set of documents. From the results in TREC 2002 and 2003, we realized the major challenging issue of recognizing relevant sentences is the lack of information used in similarity computation among sentences. In this year, we utilized the method b...

This overview paper discusses some of the major focuses of current mobile robotics research, introduces a specific application of mobile robotics — automated inspection using autonomous novelty detection — and presents one of the future challenges of mobile robotics research: that of applying quantitative methods in mobile robotics, in order to change the discipline from an empirical one to a m...

The ability of a robot to detect and respond to changes in its environment is potentially very useful , as it draws attention to new and potentially important features. We describe an algorithm for learning to filter out previously experienced stimuli to allow further concentration on novel features. The algorithm uses a model of habituation, a biological process which causes a decrement in res...

According to the results of TREC 2002, we realized the major challenge issue of recognizing relevant sentences is a lack of information used in similarity computation among sentences. In TREC 2003, NTU attempts to find relevant and novel information based on variants of employing information retrieval (IR) system. We call this methodology IR with reference corpus, which can also be considered a...

Highly automated driving requires precise models of traffic participants. Many state of the art models are currently based on machine learning techniques. Among others, the required amount of labeled data is one major challenge. An autonomous learning process addressing this problem is proposed. The initial models are iteratively refined in three steps: (1) detection and context identification,...

Previous research indicates that the amygdala and hippocampus are sensitive to novelty; however, two types of novelty can be distinguished - stimuli that are ordinary, but novel in the current context, and stimuli that are unusual. Using functional magnetic resonance imaging, we examined blood oxygen dependent level (BOLD) response of the human amygdala and hippocampus to novel, commonly seen o...

Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time of training the model. In this paper we provide stateof-...

A common setting for novelty detection assumes that labeled examples from the nominal class are available, but that labeled examples of novelties are unavailable. The standard (inductive) approach is to declare novelties where the nominal density is low, which reduces the problem to density level set estimation. In this paper, we consider the setting where an unlabeled and possibly contaminated...