Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Use of Contextual Information for Feature Ranking and Discretization
IEEE Transactions on Knowledge and Data Engineering
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
An Approach to Novelty Detection Applied to the Classification of Image Regions
IEEE Transactions on Knowledge and Data Engineering
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Context and learning in novelty detection
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Prototype-based classification
Applied Intelligence
Engineering Applications of Artificial Intelligence
Review: A review of novelty detection
Signal Processing
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Novelty detection, the ability to identify new or unknown situations that were never experienced before, is useful for intelligent systems aspiring to operate in environments where data are acquired incrementally. This characteristic is common to numerous problems in medical diagnosis and visual perception. We propose to see novelty detection as a case-based reasoning process. Our novelty-detection method is able to detect the novel situation, as well as to use the novel events for immediate reasoning. To ensure this capacity, we combine statistical and similarity inference and learning. This view of CBR takes into account the properties of data, such as the uncertainty, and the underlying concepts, such as storage, learning, retrieval, and indexing can be formalized and performed efficiently.