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39
EvidenceBased Trust A Mathematical Model Geared for Multiagent Systems
"... An evidencebased account of trust is essential for an appropriate treatment of applicationlevel interactions among autonomous and adaptive parties. Key examples include social networks and serviceoriented computing. Existing approaches either ignore evidence or only partially address the challeng ..."
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Cited by 16 (4 self)
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An evidencebased account of trust is essential for an appropriate treatment of applicationlevel interactions among autonomous and adaptive parties. Key examples include social networks and serviceoriented computing. Existing approaches either ignore evidence or only partially address the challenges of mapping evidence to trustworthiness and combining trust reports from imperfectly trusted sources. This paper develops a mathematically wellformulated approach that naturally supports discounting and combining evidencebased trust reports. This paper understands an agent Alice’s trust in an agent Bob in terms of Alice’s certainty in her belief that Bob is trustworthy. Unlike previous approaches, this paper formulates certainty in terms of evidence based on a statistical measure defined over a probability distribution of the probability of positive outcomes. This definition supports important mathematical properties ensuring correct results despite conflicting evidence: (1) for a fixed amount of evidence, certainty increases as conflict in the evidence decreases and (2) for a fixed level of conflict, certainty increases as the amount of evidence increases. Moreover, despite a subtle definition of certainty, this paper (3) establishes a bijection between evidence and trust spaces, enabling robust combination of trust reports and (4) provides an efficient algorithm for computing this bijection.
Learning Morphology with Pair Hidden Markov Models
"... In this paper I present a novel Machine Learning approach to the acquisition of stochastic string transductions based on Pair Hidden Markov Models (PHMMs), a model used in computational biology. I show how these models can be used to learn morphological processes in a variety of languages, including ..."
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Cited by 15 (1 self)
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In this paper I present a novel Machine Learning approach to the acquisition of stochastic string transductions based on Pair Hidden Markov Models (PHMMs), a model used in computational biology. I show how these models can be used to learn morphological processes in a variety of languages, including English, German and Arabic. Previous techniques for learning morphology have been restricted to languages with essentially concatenative morphology.
Study on interaction between entropy pruning and KneserNey smoothing
 in Proceedings of Interspeech
, 2010
"... The paper presents an indepth analysis of a less known interaction between KneserNey smoothing and entropy pruning that leads to severe degradation in language model performance under aggressive pruning regimes. Experiments in a datarich setup such as google.com voice search show a significant im ..."
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Cited by 14 (2 self)
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The paper presents an indepth analysis of a less known interaction between KneserNey smoothing and entropy pruning that leads to severe degradation in language model performance under aggressive pruning regimes. Experiments in a datarich setup such as google.com voice search show a significant impact in WER as well: pruning KneserNey and Katz models to 0.1 % of their original impacts speech recognition accuracy significantly, approx. 10 % relative. 1.
Explaining Naive Bayes Classifications
, 2003
"... Naïve Bayes classifiers, a popular tool for predicting the labels of query instances, are typically learned from a training set. However, since many training sets contain noisy data, a classifier user may be reluctant to blindly trust a predicted label. We present a novel graphical explanation facil ..."
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Cited by 8 (7 self)
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Naïve Bayes classifiers, a popular tool for predicting the labels of query instances, are typically learned from a training set. However, since many training sets contain noisy data, a classifier user may be reluctant to blindly trust a predicted label. We present a novel graphical explanation facility for Naïve Bayes classifiers that serves three purposes. First, it transparently explains the reasoning used by the classifier to foster user confidence in the prediction. Second, it enhances the user's understanding of the complex relationships between the features and the labels. Third, it can help the user to identify suspicious training data. We demonstrate these ideas in the context of our implemented webbased system, which uses examples from molecular biology. 1.
Hierarchical NonEmitting Markov Models
, 1998
"... We describe a simple variant of the interpolated Markov model with nonemitting state transitions and prove that it is strictly more powerful than any Markov model. More importantly, the nonemitting model outperforms the classic interpolated model on natural language texts under a wide range of expe ..."
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Cited by 6 (2 self)
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We describe a simple variant of the interpolated Markov model with nonemitting state transitions and prove that it is strictly more powerful than any Markov model. More importantly, the nonemitting model outperforms the classic interpolated model on natural language texts under a wide range of experimental conditions, with only a modest increase in computational requirements. The nonemitting model is also much less prone to overfitting.
Designing an extensible API for integrating language modeling and realization
, 2005
"... We present an extensible API for integrating language modeling and realization, describing its design and efficient implementation in the OpenCCG surface realizer. With OpenCCG, language models may be used to select realizations with preferred word orders, promote alignment with a conversational par ..."
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We present an extensible API for integrating language modeling and realization, describing its design and efficient implementation in the OpenCCG surface realizer. With OpenCCG, language models may be used to select realizations with preferred word orders, promote alignment with a conversational partner, avoid repetitive language use, and increase the speed of the bestfirst anytime search. The API enables a variety of ngram models to be easily combined and used in conjunction with appropriate edge pruning strategies. The ngram models may be of any order, operate in reverse (“righttoleft”), and selectively replace certain words with their semantic classes. Factored language models with generalized backoff may also be employed, over words represented as bundles of factors such as form, pitch accent, stem, part of speech, supertag, and semantic class.
Interactive models for semantic labeling of satellite images
 In: SPIE Annual Meeting, Earth Observing Systems Session, Proceedings of SPIE
"... We describe a system for interactive training of models for semantic labeling of land cover. The models are build based on three levels of features: 1) pixel level, 2) region level, and 3) scene level features. We developed a Bayesian algorithm and a decision tree algorithm for interactive training. ..."
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We describe a system for interactive training of models for semantic labeling of land cover. The models are build based on three levels of features: 1) pixel level, 2) region level, and 3) scene level features. We developed a Bayesian algorithm and a decision tree algorithm for interactive training. The Bayesian algorithm enables training based on pixel features. The scene level summaries of pixel features are used for fast retrieval of scenes with high/low content of features and scenes with low confidence of classification. The decision tree algorithm is based on region level features that are extracted based on 1) spectral and textural characteristics of the image, 2) shape descriptors of regions that are created through segmentation process, and 3) auxiliary information such as elevation data. The initial model can be created based on a database of ground truth and than be refined based on the feedback supplied by a data analyst who interactively trains the model using the system output and/or additional scenes. The combination of supervised and unsupervised methods provides a more complete exploration of model space. A user may detect the inadequacy of the model space and add additional features to the model. The graphical tools for the exploration of decision trees allow insight into the interaction of features used in the construction of models. The preliminary experiments show that accurate models can be build in a short time for a variety of land covers. The scalable classification techniques allow for fast searches for a specific label over a large area.
Smoothed marginal distribution constraints for language modeling
"... We present an algorithm for reestimating parameters of backoff ngram language models so as to preserve given marginal distributions, along the lines of wellknown KneserNey (1995) smoothing. Unlike KneserNey, our approach is designed to be applied to any given smoothed backoff model, including mo ..."
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Cited by 3 (1 self)
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We present an algorithm for reestimating parameters of backoff ngram language models so as to preserve given marginal distributions, along the lines of wellknown KneserNey (1995) smoothing. Unlike KneserNey, our approach is designed to be applied to any given smoothed backoff model, including models that have already been heavily pruned. As a result, the algorithm avoids issues observed when pruning KneserNey models (Siivola et al., 2007; Chelba et al., 2010), while retaining the benefits of such marginal distribution constraints. We present experimental results for heavily pruned backoff ngram models, and demonstrate perplexity and word error rate reductions when used with various baseline smoothing methods. An opensource version of the algorithm has been released as part of the OpenGrm ngram library. 1 1
Nonuniform Markov Models
, 1996
"... A statistical language model assigns probability to strings of arbitrary length. Unfortunately, it is not possible to gather reliable statistics on strings of arbitrary length from a finite corpus. Therefore, a statistical language model must decide that each symbol in a string depends on at most a ..."
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Cited by 3 (0 self)
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A statistical language model assigns probability to strings of arbitrary length. Unfortunately, it is not possible to gather reliable statistics on strings of arbitrary length from a finite corpus. Therefore, a statistical language model must decide that each symbol in a string depends on at most a small, finite number of other symbols in the string. In this report we propose a new way to model conditional independence in Markov models. The central feature of our nonuniform Markov model is that it makes predictions of varying lengths using contexts of varying lengths. Experiments on the Wall Street Journal reveal that the nonuniform model performs slightly better than the classic interpolated Markov model. This result is somewhat remarkable because both models contain identical numbers of parameters whose values are estimated in a similar manner. The only difference between the two models is how they combine the statistics of longer and shorter strings.
Using Vocabulary Knowledge in Bayesian Multinomial Estimation
, 2001
"... Estimating the parameters of sparse multinomial distributions is an important component of many statistical learning tasks. Recent approaches have used uncertainty over the vocabulary of symbols in a multinomial distribution as a means of accounting for sparsity. ..."
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Cited by 1 (0 self)
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Estimating the parameters of sparse multinomial distributions is an important component of many statistical learning tasks. Recent approaches have used uncertainty over the vocabulary of symbols in a multinomial distribution as a means of accounting for sparsity.