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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 Discovering Affinity Relationships between Personality Types ... Tshimula, Jean Marie; Chikhaoui, Belkacem; Wang, Shengrui. - : arXiv, 2022 BASE Show details 2 COVID-19: Detecting Depression Signals during Stay-At-Home Period ... Tshimula, Jean Marie; Chikhaoui, Belkacem; Wang, Shengrui. - : arXiv, 2021 BASE Show details 3 Dynamic order Markov model for categorical sequence clustering Chen, Rongbo; Sun, Haojun; Chen, Lifei; Zhang, Jianfei; Wang, Shengrui In: J Big Data (2021) Abstract: Markov models are extensively used for categorical sequence clustering and classification due to their inherent ability to capture complex chronological dependencies hidden in sequential data. Existing Markov models are based on an implicit assumption that the probability of the next state depends on the preceding context/pattern which is consist of consecutive states. This restriction hampers the models since some patterns, disrupted by noise, may be not frequent enough in a consecutive form, but frequent in a sparse form, which can not make use of the information hidden in the sequential data. A sparse pattern corresponds to a pattern in which one or some of the state(s) between the first and last one in the pattern is/are replaced by wildcard(s) that can be matched by a subset of values in the state set. In this paper, we propose a new model that generalizes the conventional Markov approach making it capable of dealing with the sparse pattern and handling the length of the sparse patterns adaptively, i.e. allowing variable length pattern with variable wildcards. The model, named Dynamic order Markov model (DOMM), allows deriving a new similarity measure between a sequence and a set of sequences/cluster. DOMM builds a sparse pattern from sub-frequent patterns that contain significant statistical information veiled by the noise. To implement DOMM, we propose a sparse pattern detector (SPD) based on the probability suffix tree (PST) capable of discovering both sparse and consecutive patterns, and then we develop a divisive clustering algorithm, named DMSC, for Dynamic order Markov model for categorical sequence clustering. Experimental results on real-world datasets demonstrate the promising performance of the proposed model. Keyword: Research URL: https://doi.org/10.1186/s40537-021-00547-2 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8651609/ BASE Hide details 4 Pattern-based causal relationships discovery from event sequences for modeling behavioral user profile in ubiquitous environments Chikhaoui, Belkacem; Wang, Shengrui; Xiong, Tengke... In: Information sciences. - New York, NY : Elsevier Science Inc. 285 (2014), 204-222 OLC Linguistik Show details 5 A PCMN neural network for isolated word recognition Yé, Haïyan; Wang, Shengrui; Robert, François In: Institut de la communication parlée <Grenoble>. Cahiers de l'ICP. Rapport de recherche. - Grenoble 1 (1991), 151-167 BLLDB Show details 6 A PCMN neural network for isolated word recognition Yé, Haïyan; Wang, Shengrui; Robert, François In: Speech communication. - Amsterdam [u.a.] : Elsevier 9 (1990) 2, 141-153 BLLDB Show details 7 A PCMN neural network for word recognition Yé, Haïyan; Wang, Shengrui; Robert, François In: Laboratoire de la Communication Parlée <Grenoble>. Bulletin du Laboratoire de la Communication Parlée. - Grenoble : ENSERG (1989) 3, 71-95 BLLDB Show details

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