排序方式: 共有14条查询结果,搜索用时 15 毫秒
1.
A Further Comparison of Splitting Rules for Decision-Tree Induction 总被引:10,自引:0,他引:10
One approach to learning classification rules from examples is to build decision trees. A review and comparison paper by Mingers (Mingers, 1989) looked at the first stage of tree building, which uses a splitting rule to grow trees with a greedy recursive partitioning algorithm. That paper considered a number of different measures and experimentally examined their behavior on four domains. The main conclusion was that a random splitting rule does not significantly decrease classificational accuracy. This note suggests an alternative experimental method and presents additional results on further domains. Our results indicate that random splitting leads to increased error. These results are at variance with those presented by Mingers. 相似文献
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Computing second derivatives in feed-forward networks: a review 总被引:6,自引:0,他引:6
The calculation of second derivatives is required by recent training and analysis techniques of connectionist networks, such as the elimination of superfluous weights, and the estimation of confidence intervals both for weights and network outputs. We review and develop exact and approximate algorithms for calculating second derivatives. For networks with |w| weights, simply writing the full matrix of second derivatives requires O(|w|(2)) operations. For networks of radial basis units or sigmoid units, exact calculation of the necessary intermediate terms requires of the order of 2h+2 backward/forward-propagation passes where h is the number of hidden units in the network. We also review and compare three approximations (ignoring some components of the second derivative, numerical differentiation, and scoring). The algorithms apply to arbitrary activation functions, networks, and error functions. 相似文献
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Documents come naturally with structure: a section contains paragraphs which itself contains sentences; a blog page contains
a sequence of comments and links to related blogs. Structure, of course, implies something about shared topics. In this paper
we take the simplest form of structure, a document consisting of multiple segments, as the basis for a new form of topic model.
To make this computationally feasible, and to allow the form of collapsed Gibbs sampling that has worked well to date with
topic models, we use the marginalized posterior of a two-parameter Poisson-Dirichlet process (or Pitman-Yor process) to handle
the hierarchical modelling. Experiments using either paragraphs or sentences as segments show the method significantly outperforms
standard topic models on either whole document or segment, and previous segmented models, based on the held-out perplexity
measure. 相似文献
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Unsupervised Object Discovery: A Comparison 总被引:1,自引:0,他引:1
Tinne Tuytelaars Christoph H. Lampert Matthew B. Blaschko Wray Buntine 《International Journal of Computer Vision》2010,88(2):284-302
The goal of this paper is to evaluate and compare models and methods for learning to recognize basic entities in images in
an unsupervised setting. In other words, we want to discover the objects present in the images by analyzing unlabeled data
and searching for re-occurring patterns. We experiment with various baseline methods, methods based on latent variable models,
as well as spectral clustering methods. The results are presented and compared both on subsets of Caltech256 and MSRC2, data
sets that are larger and more challenging and that include more object classes than what has previously been reported in the
literature. A rigorous framework for evaluating unsupervised object discovery methods is proposed. 相似文献
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Nayyar A. Zaidi Geoffrey I. Webb Mark J. Carman François Petitjean Wray Buntine Mike Hynes Hans De Sterck 《Machine Learning》2017,106(9-10):1289-1329
Recent advances have demonstrated substantial benefits from learning with both generative and discriminative parameters. On the one hand, generative approaches address the estimation of the parameters of the joint distribution—\(\mathrm{P}(y,\mathbf{x})\), which for most network types is very computationally efficient (a notable exception to this are Markov networks) and on the other hand, discriminative approaches address the estimation of the parameters of the posterior distribution—and, are more effective for classification, since they fit \(\mathrm{P}(y|\mathbf{x})\) directly. However, discriminative approaches are less computationally efficient as the normalization factor in the conditional log-likelihood precludes the derivation of closed-form estimation of parameters. This paper introduces a new discriminative parameter learning method for Bayesian network classifiers that combines in an elegant fashion parameters learned using both generative and discriminative methods. The proposed method is discriminative in nature, but uses estimates of generative probabilities to speed-up the optimization process. A second contribution is to propose a simple framework to characterize the parameter learning task for Bayesian network classifiers. We conduct an extensive set of experiments on 72 standard datasets and demonstrate that our proposed discriminative parameterization provides an efficient alternative to other state-of-the-art parameterizations. 相似文献