Sequence Covering Similarity for Symbolic Sequence Comparison
This paper introduces the sequence covering similarity, that we formally define for evaluating the similarity between a symbolic sequence (string) and a set of symbolic sequences (strings). From this covering similarity we derive a pair-wise distance to compare two symbolic sequences. We show that this covering distance is a semimetric. Few examples are given to show how this string metric in $O(n \cdot log n)$ compares with the Levenshtein's distance that is in $O(n^2)$. A final example presents its application to plagiarism detection.
Code (1)
Similar Papers 제목 키워드 기반
Sequence Covering for Efficient Host-Based Intrusion Detection
This paper introduces a new similarity measure, the covering similarity, that we formally define for evaluating the similarity between a symbolic sequence and a set of symbolic sequences. A pair-wise similarity can also …
Intrusion DetectionRecursive Binding for Similarity-Preserving Hypervector Representations of Sequences
Hyperdimensional computing (HDC), also known as vector symbolic architectures (VSA), is a computing framework used within artificial intelligence and cognitive computing that operates with distributed vector representati…
Word SimilarityShift-Equivariant Similarity-Preserving Hypervector Representations of Sequences
Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architectures (VSA), is a promising framework for the development of cognitive architectures and artificial intelligence systems, as well as for technical a…
From Dionysius Emerges Apollo -- Learning Patterns and Abstractions from Perceptual Sequences
Cognition swiftly breaks high-dimensional sensory streams into familiar parts and uncovers their relations. Why do structures emerge, and how do they enable learning, generalization, and prediction? What computational pr…
ChunkingA comparison of LSTM and GRU networks for learning symbolic sequences
We explore the architecture of recurrent neural networks (RNNs) by studying the complexity of string sequences it is able to memorize. Symbolic sequences of different complexity are generated to simulate RNN training and…
Memorization