A probabilistic neural network is applied as a tool to approximate the statistical evaluation function for a simple version of the game Tic-Tac-Toe. We solve the problem by a sequential estimation of the underlying di...
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A probabilistic neural network is applied as a tool to approximate the statistical evaluation function for a simple version of the game Tic-Tac-Toe. We solve the problem by a sequential estimation of the underlying discrete distribution mixture of product components. (c) 2005 Elsevier B.V. All rights reserved.
Modern computing systems are usually equipped with various input devices such as microphones or cameras, and hence the user of such a system can easily be identified. User identification is important in many human com...
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ISBN:
(纸本)9783319148991;9783319148984
Modern computing systems are usually equipped with various input devices such as microphones or cameras, and hence the user of such a system can easily be identified. User identification is important in many human computer interaction (HCI) scenarios, such as speech recognition, activity recognition, transcription of meeting room data or affective computing. Here personalized models may significantly improve the performance of the overall recognition system. This paper deals with audio-visual user identification. The main processing steps are segmentation of the relevant parts from video and audio streams, extraction of meaningful features and construction of the overall classifier and fusion architectures. The proposed system has been evaluated on the MOBIO dataset, a benchmark database consisting of real-world recordings collected from mobile devices, e.g. cell-phones. recognition rates of up to 92% could be achieved for the proposed audio-visual classifier system.
The estimation of probability density functions (pdf) from unlabeled data samples is a relevant (and, still open) issue in patternrecognition and machine learning. Statistical parametric and nonparametric approaches ...
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ISBN:
(纸本)9783319461823;9783319461816
The estimation of probability density functions (pdf) from unlabeled data samples is a relevant (and, still open) issue in patternrecognition and machine learning. Statistical parametric and nonparametric approaches present severe drawbacks. Only a few instances of neuralnetworks for pdf estimation are found in the literature, due to the intrinsic difficulty of unsupervised learning under the necessary integral-equals-one constraint. In turn, also such neuralnetworks do suffer from serious limitations. The paper introduces a soft-constrained algorithm for training a multilayer perceptron (MLP) to estimate pdfs empirically. A variant of the Metropolis-Hastings algorithm (exploiting the very probabilistic nature of the MLP) is used to satisfy numerically the constraint on the integral of the function learned by the MLP. The preliminary outcomes of a simulation on data drawn from a mixture of Fisher-Tippett pdfs are reported on, and compared graphically with the estimates yielded by statistical techniques, showing the viability of the approach.
Convolution kernels and recursive neuralnetworks are both suitable approaches for supervised learning when the input is a discrete structure like a labeled tree or graph. We compare these techniques in two natural la...
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Convolution kernels and recursive neuralnetworks are both suitable approaches for supervised learning when the input is a discrete structure like a labeled tree or graph. We compare these techniques in two natural language problems. In both problems, the learning task consists in choosing the best alternative tree in a set of candidates. We report about an empirical evaluation between the two methods on a large corpus of parsed sentences and speculate on the role played by the representation and the loss function. (c) 2005 Elsevier B.V. All rights reserved.
We present a computational system that combines artificialneuralnetworks and other image processing techniques to achieve teeth/palate segmentation and interdental segmentation in palatal view photographs of the upp...
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Localizing faces in images is a difficult task, and represents the first step towards the solution of the face recognition problem. Moreover, devising an effective face detection method can provide some suggestions to...
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Localizing faces in images is a difficult task, and represents the first step towards the solution of the face recognition problem. Moreover, devising an effective face detection method can provide some suggestions to solve similar object and pattern detection problems. This paper presents a novel approach to the solution of the face localization problem using Recursive neuralnetworks (RNNs). The proposed method assumes a graph-based representation of images that combines structural and symbolic visual features. Such graphs are then processed by RNNs, in order to establish the possible presence and the position of faces inside the image. A novel RNN model that can deal with graphs with labeled edges has been also exploited. Some experiments on snapshots from video sequences are reported, showing very promising results. (c) 2005 Elsevier B.V. All rights reserved.
Lifelong learning aims to develop machine learning systems that can learn new tasks while preserving the performance on previous learned tasks. In this paper we present a method to overcome catastrophic forgetting on ...
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ISBN:
(纸本)9783319999784;9783319999777
Lifelong learning aims to develop machine learning systems that can learn new tasks while preserving the performance on previous learned tasks. In this paper we present a method to overcome catastrophic forgetting on convolutional neuralnetworks, that learns new tasks and preserves the performance on old tasks without accessing the data of the original model, by selective network augmentation (SeNA-CNN). The experiment results showed that SeNA-CNN, in some scenarios, outperforms the state-of-art Learning without Forgetting algorithm. Results also showed that in some situations it is better to use SeNA-CNN instead of training a neural network using isolated learning.
In this work we propose one deep architecture to identify text and not-text regions in historical handwritten documents. In particular we adopt the U-net architecture in combination with a suitable weighted loss funct...
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ISBN:
(纸本)9783319999784;9783319999777
In this work we propose one deep architecture to identify text and not-text regions in historical handwritten documents. In particular we adopt the U-net architecture in combination with a suitable weighted loss function in order to put more emphasis on most critical areas. We define one weighted map to balance the pixel frequency among classes and to guide the training with local prior rules. In the experiments we evaluate the performance of the U-net architecture and of the weighted training on one benchmark dataset. We obtain good results using global metrics improving global and local classification scores.
Localizing faces in images is a difficult task, and represents the first step towards the solution of the face recognition problem. Moreover, devising an effective face detection method can provide some suggestions to...
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Localizing faces in images is a difficult task, and represents the first step towards the solution of the face recognition problem. Moreover, devising an effective face detection method can provide some suggestions to solve similar object and pattern detection problems. This paper presents a novel approach to the solution of the face localization problem using Recursive neuralnetworks (RNNs). The proposed method assumes a graph-based representation of images that combines structural and symbolic visual features. Such graphs are then processed by RNNs, in order to establish the possible presence and the position of faces inside the image. A novel RNN model that can deal with graphs with labeled edges has been also exploited. Some experiments on snapshots from video sequences are reported, showing very promising results. (c) 2005 Elsevier B.V. All rights reserved.
EGG records the resultant body surface potential of gastric slow waves (electrical activity);while slow waves regulate contractions of gastric muscles, it is the electrical activity we are recording, not movement (lik...
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ISBN:
(纸本)9783319461823;9783319461816
EGG records the resultant body surface potential of gastric slow waves (electrical activity);while slow waves regulate contractions of gastric muscles, it is the electrical activity we are recording, not movement (like ECG records the cardiac electrical activity, but not the contractions of the heart, even the two are essentially related).
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