scholarly journals External knowledge transfer deployment inside a simple double agent Viterbi algorithm

2021 ◽  
Author(s):  
Zied Baklouti
Keyword(s):  
Mathematical Model ◽  
Knowledge Transfer ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
State Matrix ◽  
External Knowledge ◽  
Unknown Words ◽  
Double Agent

We consider in this paper deploying external knowledge transfer inside a simple double agent Viterbi algorithm which is an algorithm firstly introduced by the author in his preprint "Hidden Markov Based Mathematical Model dedicated to Extract Ingredients from Recipe Text". The key challenge of this work lies in discovering the reason why our old model does have bad performances when it is confronted with estimating ingredient state for unknown words and see if deploying external knowledge transfer directly on calculating state matrix could be the solution instead of deploying it only on back propagating step.

scholarly journals Hidden Markov Based Mathematical Model dedicated to Extract Ingredients from Recipe Text

2019 ◽  
Author(s):  
Zied Baklouti
Keyword(s):  
Mathematical Model ◽  
Language Processing ◽  
Hidden Markov ◽  
Stochastic Methods ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Unknown Words ◽  
High Level ◽  
Speech Tagging

Natural Language Processing (NLP) is a branch of machine learning that gives the machines the ability to decode human languages. Part-of-speech tagging (POS tagging) is a preprocessing task that requires an annotated corpora. Rule-based and stochastic methods showed great results for POS tag prediction. On this work, I performed a mathematical model based on Hidden Markov structures and I obtained a high level accuracy of ingredients extracted from text recipe which is a performance greater than what traditional methods could make without unknown words consideration.

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

scholarly journals The Application of Baum-Welch Algorithm in Multistep Attack

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yanxue Zhang ◽  
Dongmei Zhao ◽  
Jinxing Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Forward Algorithm ◽  
Better Than

The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.

MATHEMATICAL MODEL OF KNOWLEDGE TRANSFER REPRESENTATION

2019 ◽  
Author(s):  
Goran Sirovatka ◽  
Vlatko Mićković ◽  
Petra Čavka
Keyword(s):  
Mathematical Model ◽  
Knowledge Transfer

Knowledge Governance Helps Minimizing the Risks of External Knowledge Transfer

2019 ◽  
pp. 379-391
Author(s):  
Reinhard Bernsteiner ◽  
Johannes Strasser ◽  
Christian Ploder ◽  
Stephan Schlögl ◽  
Thomas Dilger
Keyword(s):  
Knowledge Transfer ◽  
External Knowledge ◽  
Knowledge Governance

Location of a multinational corporation in a cluster

2016 ◽  
Vol 24 (2) ◽  
pp. 144-167 ◽  
Author(s):  
Dorota Leszczyńska ◽  
Erick Pruchnicki
Keyword(s):  
Mathematical Model ◽  
Knowledge Transfer ◽  
Tacit Knowledge ◽  
Mathematical Expression ◽  
Decision Criterion ◽  
Specific Knowledge ◽  
Content Type ◽  
The One ◽  
Effective Transfer ◽  
The Impact

Purpose A multinational company (MNC) looking to locate within a cluster is mainly interested in gaining access to scarce and highly valuable tacit knowledge. The transfer of such resources first requires sharing a certain degree of architectural and specific knowledge. This paper aims to examine the transfer of systemic technological expertise (specific tacit knowledge) that is incorporated into organisational practices (architectural knowledge). To quantify the level of knowledge transfer involved, the present study defines the architectural distance between the MNC and the cluster. Design/methodology/approach The mathematical expression of acquisition performance is inferred from a conceptual study that formulates hypotheses regarding the impact of these variables on knowledge transfer. The MNC chooses its location in such a way as to maximise this performance. Findings Applying a mathematical model to knowledge transfer between two of the MNC units helps to determine if the locally acquired knowledge could benefit other units of the MNC. Research limitations/implications The present study defines the architectural distance between the MNC and the cluster. This architectural distance is defined by a vector composed of social, organisational, cultural, institutional, technological and geographic distances between the new acquisition and its network of local partners, on the one hand, and the MNC, on the other. Knowledge transfer also depends on the business players’ trust and motivation. Further research through a quantitative study would be useful to improve the links between the proposed mathematical model and the efficiency of an MNC’s location within a cluster. Practical implications The solution to the optimisation problem allows to put forward a simple decision criterion to assist a manager who has to face the problem of an optimal location choice. Originality/value First, this study contributes to a better understanding of how knowledge transfer effects may interact with cluster effects, while explaining a subsidiary’s performance with regard to location. Second, it provides an interpretation of the concept of knowledge embeddedness by showing that the effective transfer of architectural and specific knowledge involves the prior sharing of a certain amount of this knowledge.

Top-Down Saliency Detection via Hidden Semantic Information

2014 ◽  
Vol 678 ◽  
pp. 116-119
Author(s):  
Ling Feng Yuan ◽  
Yu Liang Du ◽  
Wei Bing Wan
Keyword(s):  
Semantic Information ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Saliency Detection ◽  
Saliency Map ◽  
Histogram Of Oriented Gradient ◽  
Search Mechanism ◽  
Learned Dictionary ◽  
Salient Regions ◽  
Spatial Pyramid

Saliency detection has been applied in many cases. This paper proposes a 2D hidden Markov model (2D-HMM) which exploits the hidden semantic information of image to detect the salient regions. A spatial pyramid Histogram of Oriented Gradient (SP-HOG) descriptor is used to extract feature. After encoding the image by a learned dictionary, the 2D-viterbi algorithm is applied to inferring the saliency map. This model can depict the shapes of targets, and also it is robust to the targets’ change of posture and viewpoint. To validate the model with human’s visual search mechanism, eye track experiment is employed to train our model directly from the eye data. The results show that our model achieves a better performance than eye data. Moreover, it indicates that learning from eye track data to figure out their targets is possible.

Sign in / Sign up

Export Citation Format

Share Document