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Results 1 - 10
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4,093
Exploiting Generative Models in Discriminative Classifiers
- In Advances in Neural Information Processing Systems 11
, 1998
"... Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often resu ..."
Abstract
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Cited by 551 (9 self)
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result in classification performance superior to that of the model based approaches. An ideal classifier should combine these two complementary approaches. In this paper, we develop a natural way of achieving this combination by deriving kernel functions for use in discriminative methods such as support
A fast learning algorithm for deep belief nets
- Neural Computation
, 2006
"... We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in densely-connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a ..."
Abstract
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Cited by 970 (49 self)
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We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in densely-connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer
Imagenet classification with deep convolutional neural networks.
- In Advances in the Neural Information Processing System,
, 2012
"... Abstract We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the pr ..."
Abstract
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Cited by 1010 (11 self)
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Abstract We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than
Text Classification from Labeled and Unlabeled Documents using EM
- MACHINE LEARNING
, 1999
"... This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large qua ..."
Abstract
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Cited by 1033 (15 self)
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, and probabilistically labels the unlabeled documents. It then trains a new classifier using the labels for all the documents, and iterates to convergence. This basic EM procedure works well when the data conform to the generative assumptions of the model. However these assumptions are often violated in practice
BRITE: An approach to universal topology generation,”
- in Proceedings of the IEEE Ninth International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems,
, 2001
"... Abstract Effective engineering of the Internet is predicated upon a detailed understanding of issues such as the large-scale structure of its underlying physical topology, the manner in which it evolves over time, and the way in which its constituent components contribute to its overall function. U ..."
Abstract
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Cited by 448 (12 self)
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. Unfortunately, developing a deep understanding of these issues has proven to be a challenging task, since it in turn involves solving difficult problems such as mapping the actual topology, characterizing it, and developing models that capture its emergent behavior. Consequently, even though there are a number
Model Checking Java Programs Using Java PathFinder
, 1998
"... . This paper describes a translator called Java PathFinder (Jpf), from Java to Promela, the modeling language of the Spin model checker. Jpf translates a given Java program into a Promela model, which then can be model checked using Spin. The Java program may contain assertions, which are translated ..."
Abstract
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Cited by 386 (32 self)
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. This paper describes a translator called Java PathFinder (Jpf), from Java to Promela, the modeling language of the Spin model checker. Jpf translates a given Java program into a Promela model, which then can be model checked using Spin. The Java program may contain assertions, which
The Determinants of Credit Spread Changes.
- Journal of Finance
, 2001
"... ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are ..."
Abstract
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Cited by 422 (2 self)
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used. We conclude in Section V. 2 I. Theoretical Determinants of Credit Spread Changes So-called structural models of default provide an intuitive framework for identifying the determinants of credit spread changes. 4 These models build on the original insights of Black and Scholes (1973), who
Uncertainty-Aware Estimation of Population Abundance using Machine Learning
"... Machine Learning is widely used for mining collections, such as images, sounds, or texts, by classifying their elements into categories. Automatic classification based on supervised learning requires groundtruth datasets for modeling the elements to classify, and for testing the quality of the class ..."
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Machine Learning is widely used for mining collections, such as images, sounds, or texts, by classifying their elements into categories. Automatic classification based on supervised learning requires groundtruth datasets for modeling the elements to classify, and for testing the quality
Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart
, 2012
"... Abstract—High quality demand side management has become indispensable in the smart grid infrastructure for enhanced energy reduction and system control. In this paper, a new demand side management technique, namely, a new energy efficient scheduling algorithm, is proposed to arrange the household ap ..."
Abstract
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Cited by 9 (0 self)
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appliances for operation such that the monetary expense of a customer is minimized based on the time-varying pricing model. The proposed algorithm takes into account the uncertainties in household appliance operation time and intermittent renewable generation. Moreover, it considers the variable frequency
Context-Dependent Pre-trained Deep Neural Networks for Large Vocabulary Speech Recognition
- IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING
, 2012
"... We propose a novel context-dependent (CD) model for large vocabulary speech recognition (LVSR) that leverages recent advances in using deep belief networks for phone recognition. We describe a pretrained deep neural network hidden Markov model (DNN-HMM) hybrid architecture that trains the DNN to pr ..."
Abstract
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Cited by 254 (50 self)
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We propose a novel context-dependent (CD) model for large vocabulary speech recognition (LVSR) that leverages recent advances in using deep belief networks for phone recognition. We describe a pretrained deep neural network hidden Markov model (DNN-HMM) hybrid architecture that trains the DNN
Results 1 - 10
of
4,093
