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Automatic Analysis of Facial Expressions: The State of the Art
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2000
"... This paper surveys the past work in solving these problems. The capability of the human visual system with respect to these problems is discussed, too. It is meant to serve as an ultimate goal and a guide for determining recommendations for development of an automatic facial expression analyzer ..."
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Cited by 323 (16 self)
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This paper surveys the past work in solving these problems. The capability of the human visual system with respect to these problems is discussed, too. It is meant to serve as an ultimate goal and a guide for determining recommendations for development of an automatic facial expression analyzer
PROBEN1  a set of neural network benchmark problems and benchmarking rules
, 1994
"... Proben1 is a collection of problems for neural network learning in the realm of pattern classification and function approximation plus a set of rules and conventions for carrying out benchmark tests with these or similar problems. Proben1 contains 15 data sets from 12 different domains. All datasets ..."
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Cited by 205 (0 self)
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Proben1 is a collection of problems for neural network learning in the realm of pattern classification and function approximation plus a set of rules and conventions for carrying out benchmark tests with these or similar problems. Proben1 contains 15 data sets from 12 different domains. All datasets represent realistic problems which could be called diagnosis tasks and all but one consist of real world data. The datasets are all presented in the same simple format, using an attribute representation that can directly be used for neural network training. Along with the datasets, Proben1 defines a set of rules for how to conduct and how to document neural network benchmarking. The purpose of the problem and rule collection is to give researchers easy access to data for the evaluation of their algorithms and networks and to make direct comparison of the published results feasible. This report describes the datasets and the benchmarking rules. It also gives some basic performance measures indicating the difficulty of the various problems. These measures can be used as baselines for comparison.
A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 2000
"... We apply linear genetic programming to several diagnosis problems in medicine. An efficient algorithm is presented that eliminates intron code in linear genetic programs. This results in a significant speedup which is especially interesting when operating with complex datasets as they are occuring ..."
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Cited by 97 (13 self)
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We apply linear genetic programming to several diagnosis problems in medicine. An efficient algorithm is presented that eliminates intron code in linear genetic programs. This results in a significant speedup which is especially interesting when operating with complex datasets as they are occuring in realworld applications like medicine. We compare our results to those obtained with neural networks and argue that genetic programming is able to show similar performance in classification and generalization even within a relatively small number of generations.
Comparison Between GeometryBased and GaborWaveletsBased Facial Expression Recognition Using MultiLayer Perceptron
"... In this paper, we investigate the use of two types of features extracted from face images for recognizing facial expressions. The first type is the geometric positions of a set of fiducial points on a face. The second type is a set of multiscale and multiorientation Gabor wavelet coefficients extr ..."
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Cited by 78 (3 self)
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In this paper, we investigate the use of two types of features extracted from face images for recognizing facial expressions. The first type is the geometric positions of a set of fiducial points on a face. The second type is a set of multiscale and multiorientation Gabor wavelet coefficients extracted from the face image at the fiducial points. They can be used either independently or jointly. The architecture we developed is based on a twolayer perceptron. The recognition performance with different types of features has been compared, which shows that Gabor wavelet coefficients are much more powerful than geometric positions. Furthermore, since the first layer of the perceptron actually performs a nonlinear reduction of the dimensionality of the feature space, we have also studied the desired number of hidden units, i.e., the appropriate dimension to represent a facial expression in order to achieve a good recognition rate. It turns out that five to seven hidden units are probably...
cdec: A decoder, alignment, and learning framework for finitestate and contextfree translation models
 In Proceedings of ACL System Demonstrations
, 2010
"... We present cdec, an open source framework for decoding, aligning with, and training a number of statistical machine translation models, including wordbased models, phrasebased models, and models based on synchronous contextfree grammars. Using a single unified internal representation for translat ..."
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Cited by 75 (31 self)
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We present cdec, an open source framework for decoding, aligning with, and training a number of statistical machine translation models, including wordbased models, phrasebased models, and models based on synchronous contextfree grammars. Using a single unified internal representation for translation forests, the decoder strictly separates modelspecific translation logic from general rescoring, pruning, and inference algorithms. From this unified representation, the decoder can extract not only the 1 or kbest translations, but also alignments to a reference, or the quantities necessary to drive discriminative training using gradientbased or gradientfree optimization techniques. Its efficient C++ implementation means that memory use and runtime performance are significantly better than comparable decoders. 1
Local Gain Adaptation in Stochastic Gradient Descent
 In Proc. Intl. Conf. Artificial Neural Networks
, 1999
"... Gain adaptation algorithms for neural networks typically adjust learning rates by monitoring the correlation between successive gradients. Here we discuss the limitations of this approach, and develop an alternative by extending Sutton's work on linear systems to the general, nonlinear case. Th ..."
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Cited by 62 (12 self)
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Gain adaptation algorithms for neural networks typically adjust learning rates by monitoring the correlation between successive gradients. Here we discuss the limitations of this approach, and develop an alternative by extending Sutton's work on linear systems to the general, nonlinear case. The resulting online algorithms are computationally little more expensive than other acceleration techniques, do not assume statistical independence between successive training patterns, and do not require an arbitrary smoothing parameter. In our benchmark experiments, they consistently outperform other acceleration methods, and show remarkable robustness when faced with noni. i.d. sampling of the input space.
Neural fitted Q iteration – first experiences with a data efficient neural reinforcement learning method
 In 16th European Conference on Machine Learning
, 2005
"... Abstract. This paper introduces NFQ, an algorithm for efficient and effective training of a Qvalue function represented by a multilayer perceptron. Based on the principle of storing and reusing transition experiences, a modelfree, neural network based Reinforcement Learning algorithm is proposed. ..."
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Cited by 52 (15 self)
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Abstract. This paper introduces NFQ, an algorithm for efficient and effective training of a Qvalue function represented by a multilayer perceptron. Based on the principle of storing and reusing transition experiences, a modelfree, neural network based Reinforcement Learning algorithm is proposed. The method is evaluated on three benchmark problems. It is shown empirically, that reasonably few interactions with the plant are needed to generate control policies of high quality. 1
Apprenticeship learning using inverse reinforcement learning and gradient methods
 Proc. UAI
, 2007
"... In this paper we propose a novel gradient algorithm to learn a policy from an expert’s observed behavior assuming that the expert behaves optimally with respect to some unknown reward function of a Markovian Decision Problem. The algorithm’s aim is to find a reward function such that the resulting o ..."
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Cited by 48 (1 self)
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In this paper we propose a novel gradient algorithm to learn a policy from an expert’s observed behavior assuming that the expert behaves optimally with respect to some unknown reward function of a Markovian Decision Problem. The algorithm’s aim is to find a reward function such that the resulting optimal policy matches well the expert’s observed behavior. The main difficulty is that the mapping from the parameters to policies is both nonsmooth and highly redundant. Resorting to subdifferentials solves the first difficulty, while the second one is overcome by computing natural gradients. We tested the proposed method in two artificial domains and found it to be more reliable and efficient than some previous methods. 1
Improving the Rprop Learning Algorithm
 PROCEEDINGS OF THE SECOND INTERNATIONAL SYMPOSIUM ON NEURAL COMPUTATION (NC 2000)
, 2000
"... The Rprop algorithm proposed by Riedmiller and Braun is one of the best performing firstorder learning methods for neural networks. We introduce modifications of the algorithm that improve its learning speed. The resulting speedup is experimentally shown for a set of neural network learning tasks a ..."
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Cited by 44 (7 self)
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The Rprop algorithm proposed by Riedmiller and Braun is one of the best performing firstorder learning methods for neural networks. We introduce modifications of the algorithm that improve its learning speed. The resulting speedup is experimentally shown for a set of neural network learning tasks as well as for artificial error surfaces.