Search results for "Hidden Markov Model"
showing 10 items of 76 documents
An Innovative Statistical Tool for Automatic OWL-ERD Alignment
2016
Aligning two representations of the same domain with different expressiveness is a crucial topic in nowadays semantic web and big data research. OWL ontologies and Entity Relation Diagrams are the most widespread representations whose alignment allows for semantic data access via ontology interface, and ontology storing techniques. The term ""alignment" encompasses three different processes: OWL-to-ERD and ERD-to-OWL transformation, and OWL-ERD mapping. In this paper an innovative statistical tool is presented to accomplish all the three aspects of the alignment. The main idea relies on the use of a HMM to estimate the most likely ERD sentence that is stated in a suitable grammar, and corre…
Concurrent photo sequence organization
2012
Personal photo album organization is a highly demanding domain where advanced tools are required to manage large photo collections. In contrast to many previous works, that try to solve the problem of organizing a single user photo sequence, we present a new technique to account for the concurrent photo sequence organization problem, that is the problem of organizing multiple photo sequences taken during the same event. Given a set of sequences acquired at the same place during the same temporal window by several users using different cameras, our framework is intended to capture the evolution of the event and groups photos based on temporal proximity and visual content. The method automati…
Hankelet-based dynamical systems modeling for 3D action recognition
2015
This paper proposes to model an action as the output of a sequence of atomic Linear Time Invariant (LTI) systems. The sequence of LTI systems generating the action is modeled as a Markov chain, where a Hidden Markov Model (HMM) is used to model the transition from one atomic LTI system to another. In turn, the LTI systems are represented in terms of their Hankel matrices. For classification purposes, the parameters of a set of HMMs (one for each action class) are learned via a discriminative approach. This work proposes a novel method to learn the atomic LTI systems from training data, and analyzes in detail the action representation in terms of a sequence of Hankel matrices. Extensive eval…
Analysis of clickstream data with mixture hidden markov models
2021
clickstream data sono un’importante fonte di informazioni per l’ecommerce, sebbene non siano semplici da gestire e convertire queste informazioni in un reale vantaggio competitivo non e un compito banale. In questo articolo, consid- ` eriamo l’applicazione dei mixture hidden Markov model a dati relativi al flusso di clickstream estratti dal portale e-commerce di un’azienda di servizi turistici. Sono stati individuati cluster relativi al comportamento di navigazione degli utenti e alla loro posizione geografica che forniscono indicazioni importanti per lo sviluppo di nuove strategie di business. Clickstream data is an important source of information for businesses, however it is not easy to …
Usage of HMM-Based Speech Recognition Methods for Automated Determination of a Similarity Level Between Languages
2019
The problem of automated determination of language similarity (or even defining of a distance on the space of languages) could be solved in different ways – working with phonetic transcriptions, with speech recordings or both of them. For the recordings, we propose and test a HMM-based one: in the first part of our article we successfully try language detection, afterwards we are trying to calculate distances between HMM-based models, using different metrics and divergences. The Kullback-Leibler divergence is the only one we got good results with – it means that the calculated distances between languages correspond to analytical understanding of similarity between them. Even if it does not …
On Hagelbarger’s and Shannon’s matching pennies playing machines
2020
Abstract In the 1950s, Hagelbarger’s Sequence Extrapolating Robot (SEER) and Shannon’s Mind-Reading Machine (MRM) were the state-of-the-art research results in playing the well-known “matching pennies” game. In our research we perform a software implementation for both machines in order to test the common statement that MRM, even simpler, beats SEER. Also, we propose a simple contextual predictor (SCP) and use it to compete with SEER and MRM. As expected, experimental results proves the claimed MRM superiority over SEER and even the SCP’s superiority over both SEER and MRM. At the end, we draw some conclusions and propose further research ideas, like the use of mixing models methods and the…
Bayesian hierarchical Poisson models with a hidden Markov structure for the detection of influenza epidemic outbreaks
2015
Considerable effort has been devoted to the development of statistical algorithms for the automated monitoring of influenza surveillance data. In this article, we introduce a framework of models for the early detection of the onset of an influenza epidemic which is applicable to different kinds of surveillance data. In particular, the process of the observed cases is modelled via a Bayesian Hierarchical Poisson model in which the intensity parameter is a function of the incidence rate. The key point is to consider this incidence rate as a normal distribution in which both parameters (mean and variance) are modelled differently, depending on whether the system is in an epidemic or non-epide…
Bayesian Markov switching models for the early detection of influenza epidemics
2008
The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In this paper, a Markov switching model is introduced to determine the epidemic and non-epidemic periods from influenza surveillance data: the process of differenced incidence rates is modelled either with a first-order autoregressive process or with a Gaussian white-noise process depending on whether the system is in an epidemic or in a non-epidemic phase. The transition between phases of the disease is modelled as a Markovian process. Bayesian inference is carried out on the former model to detect influenza epidemics at the very moment of their onset. Moreover, t…
A Hidden Markov Model for Automatic Generation of ER Diagrams from OWL Ontology
2014
Connecting ontological representations and data models is a crucial need in enterprise knowledge management, above all in the case of federated enterprises where corporate ontologies are used to share information coming from different databases. OWL to ERD transformations are a challenging research field in this scenario, due to the loss of expressiveness arising when OWL axioms have to be represented using ERD notation. In this paper we propose an innovative technique for estimating the most likely composition of ERD constructs that correspond to a given sequence of OWL axioms. We model such a process using a Hidden Markov Model (HMM) where the OWL inputs are the observable states, while E…
Learning the structure of HMM's through grammatical inference techniques
2002
A technique is described in which all the components of a hidden Markov model are learnt from training speech data. The structure or topology of the model (i.e. the number of states and the actual transitions) is obtained by means of an error-correcting grammatical inference algorithm (ECGI). This structure is then reduced by using an appropriate state pruning criterion. The statistical parameters that are associated with the obtained topology are estimated from the same training data by means of the standard Baum-Welch algorithm. Experimental results showing the applicability of this technique to speech recognition are presented. >