Maximum Entropy (MaxEnt) model has been proven to be a very effective approach in the topic classification task, where a specific topic from a pre-defined topic set will be assigned to each sentence. Although it is originally developed based on the motivation of maximizing the conditional probability entropy under certain constraints, MaxEnt model is indeed an exponential distribution model tha...

capra aegagrus is one of the most highly-valued animals of iran. this species is also consideredas one of the main food sources for the acinonyx jubatus venaticus, which are critically endangered.seasonalhabitat use of wild goat was studied from january 2011 to september 2012 in dareh anjir wildlife refuge,yazd, iran. we developed maximum entropy models in three seasons.this study aims to deter...

The aim of this work is to develop a practical framework, which extends the classical Hidden Markov Models (HMM) for continuous speech recognition based on the Maximum Entropy (MaxEnt) principle. The MaxEnt models can estimate the posterior probabilities directly as with Hybrid NN/HMM connectionist speech recognition systems. In particular, a new acoustic modelling based on discriminative MaxEn...

Through completing an underspeciied probability model, Maximum Entropy (MaxEnt) supports non-monotonic inferences. Some major aspects of how this is done by MaxEnt can be understood from the background of two principles of rational decision: the concept of Indiierence and the concept of Independence. In a formal speciication MaxEnt can be viewed as (conservative) extension of these principles; ...

ix CHAPTER 1: INTRODUCTION 1 1.1 Thesis Organization 7 CHAPTER 2: RELATED WORK 8 2.1 Types of Species Distribution Models 8 2.2 The MAXENT Model and Applications 10 CHAPTER 3: METHODS AND DATA 15 3.1 Typical Species Distribution Model Workflow 15 3.2 Presence Data 17 3.3 Explanatory Variables 17 3.4 MAXENT Model Runs 22 3.5 Assessment of Model and Variable Performance 23 CHAPTER 4: RESULTS 25 4...

Hierarchical phrase-based (HPB) translation provides a powerful mechanism to capture both short and long distance phrase reorderings. However, the phrase reorderings lack of contextual information in conventional HPB systems. This paper proposes a contextdependent phrase reordering approach that uses the maximum entropy (MaxEnt) model to help the HPB decoder select appropriate reordering patter...

Through completing an underspeciied probability model, Maximum Entropy (MaxEnt) supports non-monotonic inferences. Some major aspects of how this is done by MaxEnt can be understood from the background of two principles of rational decision: the concept of Indiierence and the concept of Independence. In a formal speciication MaxEnt can be viewed as (conservative) extension of these principles; ...

We propose a novel reordering model for phrase-based statistical machine translation (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs). The model provides content-dependent, hierarchical phrasal reordering with generalization based on features automatically learned from a real-world bitext. We present an algorithm to extract all reorderi...