Information entropy features are often used for radiation source signal recognition, but due to the information entropy is very sensitive to noise, so this method has greater recognition rate changes with the SNR. This paper putting forward a viable recognition based on Entropy and cloud model. using cloud model to extract secondary features of signals, build radiation source signal’s entropy a...

In this paper, we propose an approach for inferring semantic role using subcategorization frames and maximum entropy model. Our approach aims to use the sub-categorization information of the verb to label the mandatory arguments of the verb in various possible ways. The ambiguity between the assignment of roles to mandatory arguments is resolved using the maximum entropy model. The unlabelled m...

In this paper, a Maximum Entropy Markov Model (MEMM) for dialog state tracking is proposed to efficiently handle user goal evolvement in two steps. The system first predicts the occurrence of a user goal change based on linguistic features and dialog context for each dialog turn, and then the proposed model could utilize this user goal change information to infer the most probable dialog state ...

In this paper we describe and evaluate several statistical models for the task of realization ranking, i.e. the problem of discriminating between competing surface realizations generated for a given input semantics. Three models (and several variants) are trained and tested: an n-gram language model, a discriminative maximum entropy model using structural information (and incorporating the lang...

Entropy measures have been widely used to quantify the complexity of theoretical and experimental dynamical systems. In this paper, the value of using entropy measures to characterize human locomotion is demonstrated based on their construct validity, predictive validity in a simple model of human walking and convergent validity in an experimental study. Results show that four of the five consi...

Maximum entropy models are often used to describe supply and demand behavior in urban transportation and land use systems. However, they have been criticized for not representing behavioral rules of system agents and because their parameters seems to adjust only to modeler-imposed constraints. In response, it is demonstrated that the solution to the entropy maximization problem with linear cons...

We show that the Maximum Entropy principle (E.T. Jaynes, [8]) has a natural description in terms of Morse Families of a Lagrangian submanifold. This geometric approach becomes useful when dealing with the M.E.P. with nonlinear constraints. Examples are presented using the Ising and Potts models of a ferromagnetic material.

The language models used for automatic speech recognition (ASR) are often based on very simple Markov models. This paper presents an overview of a more powerful modelling technique, Maximum Entropy (ME), and its application in langauge modelling. Preliminary results indicate that ME models are viable for this task, and perform slightly better than the traditional models.

We analyzed the entropy production in the majority-vote model by using a mean-field approximation and Monte Carlo simulations. The dynamical rules of the model do not obey detailed balance so that entropy is continuously being produced. This nonequilibrium stochastic model is known to have a critical behavior belonging to the universality class of the equilibrium Ising model. We show that the e...