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Discriminative Training of Hidden Markov Models
, 1998
"... vi Abbreviations vii Notation viii 1 Introduction 1 2 Hidden Markov Models 4 2.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 HMM Modelling Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 HMM Topology . . . . . . . . . ..."
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Cited by 20 (0 self)
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vi Abbreviations vii Notation viii 1 Introduction 1 2 Hidden Markov Models 4 2.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 HMM Modelling Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 HMM Topology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.4 Finding the Best Transcription . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.5 Setting the Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3 Objective Functions 19 3.1 Properties of Maximum Likelihood Estimators . . . . . . . . . . . . . . . . . . . 19 3.2 Maximum Likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3 Maximum Mutual Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.4 Frame Discrimination . . . . . . . . . . . . . . . . ....
Discriminative training for largevocabulary speech recognition using minimum classification error
 IEEE Trans. Audio, Speech, Language Process
, 2007
"... This work is copyrighted by the IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted ..."
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Cited by 12 (1 self)
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This work is copyrighted by the IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted
Realtime Object Classification and Novelty Detection for Collaborative Video Surveillance
 In Proceedings of the International Joint Conference on Neural Networks
, 2002
"... To conduct realtime video surveillance using lowcost commercial offtheshelf hardware, system designers typically define the classifiers prior to the deployment of the system so that the performance of the system can be optimized for a particular mission. This implies the system is restricted to ..."
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To conduct realtime video surveillance using lowcost commercial offtheshelf hardware, system designers typically define the classifiers prior to the deployment of the system so that the performance of the system can be optimized for a particular mission. This implies the system is restricted to interpreting activity in the environment in terms of the original context specified. Ideally the system should allow the user to provide additional context in an incremental fashion as conditions change. Given the volumes of data produced by the system, it is impractical for the user to periodically review and label a significant fraction of the available data. We explore a strategy for designing a realtime object classification process that aids the user in identifying novel, informative examples for efficient incremental learning.
Distributed Surveillance and Reconnaissance Using Multiple Autonomous ATVs: CyberScout
 IEEE Transactions on Robotics and Automation
, 2002
"... The objective of the CyberScout project is to develop an autonomous surveillance and reconnaissance system using a network of allterrain vehicles. In this paper, we focus on two facets of this system: 1) vision for surveillance and 2) autonomous navigation and dynamic path planning. In the area of ..."
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Cited by 7 (0 self)
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The objective of the CyberScout project is to develop an autonomous surveillance and reconnaissance system using a network of allterrain vehicles. In this paper, we focus on two facets of this system: 1) vision for surveillance and 2) autonomous navigation and dynamic path planning. In the area of visionbased surveillance, we have developed robust, efficient algorithms to detect, classify, and track moving objects of interest (person, people, or vehicle) with a static camera. Adaptation through feedback from the classifier and tracker allow the detector to use grayscale imagery, but perform as well as prior colorbased detectors. We have extended the detector using scene mosaicing to detect and index moving objects when the camera is panning or tilting. The classification algorithm performs well (less than 8% error rate for all classes) with coarse inputs (20 20pixel binary image chips), has unparalleled rejection capabilities (rejects 72% of spurious detections), and can flag novel moving objects. The tracking algorithm achieves highly accurate (96%) frametoframe correspondence for multiple moving objects in cluttered scenes by determining the discriminant relevance of object features. We have also developed a novel mission coordination architecture, CPAD (Checkpoint/Priority/Action Database), which performs path planning via checkpoint and dynamic priority assignment, using statistical estimates of the environment's motion structure. The motion structure is used to make both preplanning and reactive behaviors more efficient by applying global context. This approach is more computationally efficient than centralized approaches and exploits robot cooperation in dynamic environments better than decoupled approaches.
Efficient Autonomous Learning for Statistical Pattern Recognition
"... We describe a neural network learning algorithm that implements differential learning in a generalized backpropagation framework. The algorithm regulates model complexity during the learning procedure, generating the best lowcomplexity approximation to the Bayesoptimal classifier allowed by the tr ..."
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We describe a neural network learning algorithm that implements differential learning in a generalized backpropagation framework. The algorithm regulates model complexity during the learning procedure, generating the best lowcomplexity approximation to the Bayesoptimal classifier allowed by the training sample. It learns to recognize handwritten digits of the AT&T DB1 database. Learning is done with little human intervention. The algorithm generates a simple neural network classifier from the benchmark partitioning of the database; the classifier has 650 total parameters and exhibits a test sample error rate of 1.3%. 1 INTRODUCTION Recent advances in machine learning theory make it possible to generate pattern classifiers that are consistently robust estimates of the Bayesoptimal (i.e., minimum probabilityoferror) classifier. Moreover, these advances guarantee good approximations to the Bayesoptimal classifier from models with the minimum functional complexity (e.g., the fewest p...
Differential Theory of Learning for Efficient Neural Network Pattern Recognition
 in Applications of Artificial Neural Networks IV
, 1965
"... We describe a new theory of differential learning by which a broad family of pattern classifiers (including many wellknown neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generalize well to unseen test examples and ..."
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We describe a new theory of differential learning by which a broad family of pattern classifiers (including many wellknown neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generalize well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closedform analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts. 1 DIFFERENTIAL LEARNING A differentiable supervised classifier is one that learns an inputtooutput mapping by adjusting a set of internal parameters ` via...
Differentially Generated Neural Network Classifiers Are Efficient
"... Differential learning for statistical pattern classification is described in [5]; it is based on the classification figureofmerit (CFM) objective function described in [9, 5]. We prove that differential learning is asymptotically efficient, guaranteeing the best generalization allowed by the choic ..."
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Differential learning for statistical pattern classification is described in [5]; it is based on the classification figureofmerit (CFM) objective function described in [9, 5]. We prove that differential learning is asymptotically efficient, guaranteeing the best generalization allowed by the choice of hypothesis class (see below) as the training sample size grows large, while requiring the least classifier complexity necessary for Bayesian (i.e., minimum probabilityoferror) discrimination. Moreover, differential learning almost always guarantees the best generalization allowed by the choice of hypothesis class for small training sample sizes.