Exponential distribution is ubiquitous in the framework of multi-agent systems. Usually, it appears as an equilibrium state in the asymptotic time evolution of statistical systems. It has been explained from very different perspectives. In statistical physics, it is obtained from the principle of maximum entropy [1]. In the same context, it can also be derived without any consideration about in...

drought is a major environmental disaster in many parts of the world. knowledge about the timing, severity and extentof drought can aid planning and decision-making. drought indices derived from in-situ meteorological data have coarsespatial and temporal resolutions, thus, obtaining a real-time drought condition over a large area is difficult. this studyused advanced very high resolution radiom...

We propose an efficient way to train maximum entropy language models (MELM) and neural network language models (NNLM). The advantage of the proposed method comes from a more robust and efficient subsampling technique. The original multi-class language modeling problem is transformed into a set of binary problems where each binary classifier predicts whether or not a particular word will occur. ...

land use change may influence many natural phenomena and ecological processes, including runoff, soil erosion, sedimentation and soil conditions. decreasing of forest area in the north of iran is one of the critical problems in recent years. the aims of this study are to detect land use changes between 1967 to 2002 using satellite images of land sat 7 etm+ (2002), aerial photos and digital topo...

An approach to auctions and bidding is founded on observations and expectations of the opponents' behavior and not on assumptions concerning the opponents' motivations or internal reasoning. The approach draws ideas from information theory. A bidding agent employs maximum entropy inference to determine its actions on the basis of this uncertain data. Maximum entropy inference may be applied bot...

Abstract. The restrictions of the analysis of natural processes which are observed at any point in space or time to a purely spatial or purely temporal domain may cause loss of information and larger prediction errors. Moreover, the arbitrary combinations of purely spatial and purely temporal models may not yield valid models for the space-time domain. For such processes the variation can be ch...

We discuss an unsupervised learning method which is driven by an information theoretic based criterion. Information theoretic based learning has been examined by several authors Linsker [2, 31, Bell and Sejnowski [5], Deco and Obradovic [l], and Viola et aZ[6]. The method we discuss differs from previous work in that it is extensible to a feed-forward multi-layer perceptron with an arbitrary nu...