261372
Learning Mixture Models with the Latent Maximum Entropy Principle
We present a new approach to estimating mixture models based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes' maximum entropy principle and from standard maximum likelihood estimation. We demonstrate the LME principle by deriving new algorithms for mixture model estimation, and show how robust new variants of the EM algorithm can be developed. Our experiments show that estimation based on LME generally yields better results than maximum likelihood estimation, particularly when inferring latent variable models from small amounts of data.
662359
Language and Task Independent Text Categorization with Simple Language Models
We present a simple method for language independent and task independent text categorization learning, based on character-level n-gram language models. Our approach uses simple information theoretic principles and achieves effective performance across a variety of languages and tasks without requiring feature selection or extensive pre-processing. To demonstrate the language and task independence of the proposed technique, we present experimental results on several languages---Greek, English, Chinese and Japanese---in several text categorization problems---language identification, authorship attribution, text genre classification, and topic detection. Our experimental results show that the simple approach achieves state of the art performance in each case.
7958668
Boltzmann Machine Learning with the Latent Maximum Entropy Principle
We present a new statistical learning paradigm for Boltzmann machines based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes' maximum entropy principle and from standard maximum likelihood estimation. We demonstrate the LME principle by deriving new algorithms for Boltzmann machine parameter estimation, and show how a robust and rapidly convergent new variant of the EM algorithm can be developed. Our experiments show that estimation based on LME generally yields better results than maximum likelihood estimation when inferring models from small amounts of data.
210649
Combining Statistical Language Models via the Latent Maximum Entropy Principle
We present a unified probabilistic framework for statistical language modeling which can simultaneously incorporate various aspects of natural language, such as local word interaction, syntactic structure and semantic document information. Our approach is based on a recent statistical inference principle we have proposed—the latent maximum entropy principle—which allows relationships over hidden features to be effectively captured in a unified model. Our work extends previous research on maximum entropy methods for language modeling, which only allow observed features to be modeled. The ability to conveniently incorporate hidden variables allows us to extend the expressiveness of language models while alleviating the necessity of pre-processing the data to obtain explicitly observed features. We describe efficient algorithms for marginalization, inference and normalization in our extended models. We then use these techniques to combine two standard forms of language models: local lexical models (Markov N-gram models) and global document-level semantic models (probabilistic latent semantic analysis). Our experimental results on the Wall Street Journal corpus show that we obtain a 18.5% reduction in perplexity compared to the baseline tri-gram model with Good-Turing smoothing.
89684
Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling
We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled and unlabeled training data. Our approach is based on extending the minimum entropy regularization framework to the structured prediction case, yielding a training objective that combines unlabeled conditional entropy with labeled conditional likelihood. Although the training objective is no longer concave, it can still be used to improve an initial model (e.g. obtained from supervised training) by iterative ascent. We apply our new training algorithm to the problem of identifying gene and protein mentions in biological texts, and show that incorporating unlabeled data improves the performance of the supervised CRF in this case.
12212021
Variational Bayesian image modelling
We present a variational Bayesian framework for performing inference, density estimation and model selection in a special class of graphical models---Hidden Markov Random Fields (HMRFs). HMRFs are particularly well suited to image modelling and in this paper, we apply them to the problem of image segmentation. Unfortunately, HMRFs are notoriously hard to train and use because the exact inference problems they create are intractable. Our main contribution is to introduce an efficient variational approach for performing approximate inference of the Bayesian formulation of HMRFs, which we can then apply to the density estimation and model selection problems that arise when learning image models from data. With this variational approach, we can conveniently tackle the problem of image segmentation. We present experimental results which show that our technique outperforms recent HMRF-based segmentation methods on real world images.
11681399
Security of an Efficient ID-Based Authenticated Key Agreement Protocol from Pairings
Authenticated key agreement protocols are essential for secure communications in open and distributed environments. In 2004, Ryu et al. proposed an efficient two-party identity-based authenticated key agreement protocol based on pairings. However, in this paper, we demonstrate that their protocol is vulnerable to a key-compromise impersonation attack. The attacking scenario is described in details. Furthermore,we point out that their protocol provides the property of deniability and at the same time it is the mechanism used to achieve deniability that allows the key-compromise impersonation attack.
6620052
ScatterType: a legible but hard-to-segment CAPTCHA
The ScatterType CAPTCHA (completely automated public Turing tests to tell computers and humans apart), designed to resist character-segmentation attacks and shown to be highly legible to human readers, is analyzed for vulnerabilities and is offered for experiments in automatic attack. As introduced in Baird and Riopka (2005), 'ScatterType' challenges are images of machine-print text whose characters are cut into pieces which then drift apart, in an attempt to frustrate segment-then-recognize computer vision attacks. Analysis of experimental human legibility data has shown that better than 95% correct legibility can be achieved through judicious choice of the pseudorandom generating parameters (Baird et al., 2005). That analysis is summarized and discussed here as motivation for a discussion of potential vulnerabilities. An invitation to attack ScatterType is offered.
18505600
FCM-Based Model Selection Algorithms for Determining the Number of Clusters
Clustering is an important research topic that has practical applications in many fields. It has been demonstrated that fuzzy clustering, using algorithms such as the fuzzy C-means (FCM), has clear advantages over crisp and probabilistic clustering methods. Like most clustering algorithms, however, FCM and its derivatives need the number of clusters in the given data set as one of their initializing parameters. The main goal of this paper is to develop an effective fuzzy algorithm for automatically determining the number of clusters. After a brief review of the relevant literature, we present a new algorithm for determining the number of clusters in a given data set and a new validity index for measuring the "goodness" of clustering. Experimental results and comparisons are given to illustrate the performance of the new algorithm.
1171329
Hidden Conditional Random Fields for Gesture Recognition
We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex underlying structure, and models that can incorporate hidden structures have proven to be advantageous for recognition tasks. Most existing approaches to gesture recognition with hidden states employ a Hidden Markov Model or suitable variant (e.g., a factored or coupled state model) to model gesture streams; a significant limitation of these models is the requirement of conditional independence of observations. In addition, hidden states in a generative model are selected to maximize the likelihood of generating all the examples of a given gesture class, which is not necessarily optimal for discriminating the gesture class against other gestures. Previous discriminative approaches to gesture sequence recognition have shown promising results, but have not incorporated hidden states nor addressed the problem of predicting the label of an entire sequence. In this paper, we derive a discriminative sequence model with a hidden state structure, and demonstrate its utility both in a detection and in a multi-way classification formulation. We evaluate our method on the task of recognizing human arm and head gestures, and compare the performance of our method to both generative hidden state and discriminative fully-observable models.
27992571
Segmentation of SAR images
Abstract This paper presents a new algorithm for segmentation of SAR images based on threshold estimation using the histogram. The speckle distribution in the SAR image is modeled by a Gamma function. Thus, the SAR image histogram exhibits a combination of Gamma distributions. The maximum likelihood technique is therefore used to estimate the histogram parameters. This technique requires knowledge of the number of modes of the histogram, the number of looks of the SAR image, and the initial parameters of the histogram. The second derivative of the histogram is used to estimate the number of modes. We use two methods to estimate the number of looks. Initial parameters are estimated at the maximum of the Gamma function. Thresholds are selected at the valleys of a multi-modal histogram by minimizing the discrimination error between the classes of pixels in the image. The algorithm is applied to several RADARSAT SAR images with different number of looks. The results obtained are promising.