Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
47,874
A Classification of Models for Concurrency (Extended Abstract)
- Handbook of Logic in Computer Science
, 1993
"... Models for concurrency can be classified with respect to the three relevant parameters: behaviour/system, interleaving/noninterleaving, linear /branching time. When modelling a process, a choice concerning such parameters corresponds to choosing the level of abstraction of the resulting semantics. T ..."
Abstract
- Add to MetaCart
. The classifications are formalised through the medium of category theory. Introduction From its beginning, many efforts in the development of the theory of concurrency have been devoted to the study of suitable models for concurrent and distributed processes, and to the formal understanding of their semantics. As a
Unsupervised Models for Named Entity Classification
- In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora
, 1999
"... This paper discusses the use of unlabeled examples for the problem of named entity classification. A large number of rules is needed for coverage of the domain, suggesting that a fairly large number of labeled examples should be required to train a classifier. However, we show that the use of unlabe ..."
Abstract
-
Cited by 542 (4 self)
- Add to MetaCart
This paper discusses the use of unlabeled examples for the problem of named entity classification. A large number of rules is needed for coverage of the domain, suggesting that a fairly large number of labeled examples should be required to train a classifier. However, we show that the use
A comparison of event models for Naive Bayes text classification
, 1998
"... Recent work in text classification has used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey ..."
Abstract
-
Cited by 1025 (26 self)
- Add to MetaCart
Recent work in text classification has used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e
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
-
Cited by 1010 (11 self)
- Add to MetaCart
-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012
A classification and comparison framework for software architecture description languages
- IEEE Transactions on Software Engineering
, 2000
"... Software architectures shift the focus of developers from lines-of-code to coarser-grained architectural elements and their overall interconnection structure. Architecture description languages (ADLs) have been proposed as modeling notations to support architecture-based development. There is, howev ..."
Abstract
-
Cited by 855 (59 self)
- Add to MetaCart
, however, little consensus in the research community on what is an ADL, what aspects of an architecture should be modeled in an ADL, and which of several possible ADLs is best suited for a particular problem. Furthermore, the distinction is rarely made between ADLs on one hand and formal specification
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
-
Cited by 1033 (15 self)
- Add to MetaCart
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
A Model of Investor Sentiment
- Journal of Financial Economics
, 1998
"... Recent empirical research in finance has uncovered two families of pervasive regularities: underreaction of stock prices to news such as earnings announcements, and overreaction of stock prices to a series of good or bad news. In this paper, we present a parsimonious model of investor sentiment, or ..."
Abstract
-
Cited by 777 (32 self)
- Add to MetaCart
, or of how investors form beliefs, which is consistent with the empirical findings. The model is based on psychological evidence and produces both underreaction and overreaction for a wide range of parameter values. � 1998 Elsevier Science S.A. All rights reserved. JEL classification: G12; G14
The 4+1 view model of architecture
- IEEE SOFTWARE
, 1995
"... The 4+1 View Model organizes a description of a software architecture using five concurrent views, each of which
addresses a specific set of concerns. Architects capture their design decisions in four views and use the fifth view to illustrate and validate them. ..."
Abstract
-
Cited by 563 (4 self)
- Add to MetaCart
The 4+1 View Model organizes a description of a software architecture using five concurrent views, each of which
addresses a specific set of concerns. Architects capture their design decisions in four views and use the fifth view to illustrate and validate them.
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
-
Cited by 551 (9 self)
- Add to MetaCart
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 Strategic Model of Social and Economic Networks
- CMSEMS Discussion Paper 1098, Northwestern University, revised
, 1995
"... We study the stability and efficiency of social and economic networks, when selfinterested individuals can form or sever links. First, for two stylized models, we characterize the stable and efficient networks. There does not always exist a stable network that is efficient. Next, we show that this t ..."
Abstract
-
Cited by 664 (22 self)
- Add to MetaCart
We study the stability and efficiency of social and economic networks, when selfinterested individuals can form or sever links. First, for two stylized models, we characterize the stable and efficient networks. There does not always exist a stable network that is efficient. Next, we show
Results 1 - 10
of
47,874
