標(biāo)題: Titlebook: Combining Artificial Neural Nets; Ensemble and Modular Amanda J. C. Sharkey Book 1999 Springer-Verlag London Limited 1999 Ensembl.cognition [打印本頁] 作者: Harrison 時(shí)間: 2025-3-21 17:16
書目名稱Combining Artificial Neural Nets影響因子(影響力)
作者: delta-waves 時(shí)間: 2025-3-21 23:36
Mixtures of ,,es of mixture modelling. The chapter reviews (i) mixtures of distributions from the exponential family, (ii) hidden Markov models, (iii) Mixtures of Experts, (iv) mixtures of marginal models, (v) mixtures of Cox models, (vi) mixtures of factor models, and (vii) mixtures of trees.作者: 設(shè)想 時(shí)間: 2025-3-22 02:08 作者: Throttle 時(shí)間: 2025-3-22 08:19 作者: Wordlist 時(shí)間: 2025-3-22 09:34 作者: 過于平凡 時(shí)間: 2025-3-22 16:19
,Lieferantenn?he: S?R Rusche GmbH,redicted (classification). We review some of the recent developments that seem notable to us. These include bagging, boosting, and arcing. The basic algorithm used in our empirical studies is tree-structured CART but a variety of other algorithms have also been used to form ensembles.作者: 過于平凡 時(shí)間: 2025-3-22 17:43 作者: 現(xiàn)暈光 時(shí)間: 2025-3-23 01:05
Combining Predictors,redicted (classification). We review some of the recent developments that seem notable to us. These include bagging, boosting, and arcing. The basic algorithm used in our empirical studies is tree-structured CART but a variety of other algorithms have also been used to form ensembles.作者: Minuet 時(shí)間: 2025-3-23 04:59 作者: NOCT 時(shí)間: 2025-3-23 06:58 作者: 興奮過度 時(shí)間: 2025-3-23 13:28 作者: arbovirus 時(shí)間: 2025-3-23 14:11 作者: 貪婪地吃 時(shí)間: 2025-3-23 20:07
1431-6854 ifferent nets trained on the same task; the modular approachThe past decade could be seen as the heyday of neurocomputing: in which the capabilities of monolithic nets have been well explored and exploited. The question then is where do we go from here? A logical next step is to examine the potentia作者: parallelism 時(shí)間: 2025-3-24 00:37 作者: 消耗 時(shí)間: 2025-3-24 04:42
Self-Organised Modular Neural Networks for Encoding Data,-dimensional data space is broken up into a number of lowdimensional subspaces, each of which is separately encoded. This type of factorial encoder emerges through a process of self-organisation, provided that the input data lies on a curved manifold, as is indeed the case in image processing applications.作者: 絕種 時(shí)間: 2025-3-24 08:14 作者: Accomplish 時(shí)間: 2025-3-24 11:28 作者: Lyme-disease 時(shí)間: 2025-3-24 18:03 作者: Habituate 時(shí)間: 2025-3-24 22:30
Model Selection of Combined Neural Nets for Speech Recognition,ask and in digit recognition over a noisy telephone line. Bootstrap estimates of minimum MSE allow selection of regression models that improve system recognition performance. The procedure allows a flexible strategy for dealing with inter-speaker variability without requiring an additional validatio作者: GRACE 時(shí)間: 2025-3-25 01:35
1431-6854 ntages of modular design and reuse advocated by object-oriented programmers. And it is not surprising to find that the same principles can be usefully applied in the field of neurocomput- ing as well, although finding the best way of adapting them is a subject of on-going research.978-1-85233-004-0978-1-4471-0793-4Series ISSN 1431-6854 作者: 煞費(fèi)苦心 時(shí)間: 2025-3-25 03:30
Prinz Alwaleed bin Talal – der Anleger-Prinze optimal combination-weights for combining the networks. We describe an approach for treating collinearity by the proper selection of the component networks, and test two algorithms for selecting the components networks in order to improve the generalisation ability of the ensemble. We present expe作者: 樸素 時(shí)間: 2025-3-25 08:19
Prinz Alwaleed bin Talal – der Anleger-Prinzd. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based non-linear combiners, we derive expressions that indicate how much the median, the maximum and in general the .作者: HILAR 時(shí)間: 2025-3-25 12:35 作者: Flu表流動(dòng) 時(shí)間: 2025-3-25 17:25 作者: 歌唱隊(duì) 時(shí)間: 2025-3-25 23:19 作者: 饑荒 時(shí)間: 2025-3-26 01:23 作者: 外形 時(shí)間: 2025-3-26 06:08
Boosting Using Neural Networks,ng works by iteratively constructing weak learners whose training set is conditioned on the performance of the previous members of the ensemble. In classification, we train neural networks using stochastic gradient descent and in regression, we train neural networks using conjugate gradient descent.作者: 變形詞 時(shí)間: 2025-3-26 11:32
A Genetic Algorithm Approach for Creating Neural Network Ensembles,prediction. An effective ensemble should consist of a set of networks that are not only highly correct, but ones that make their errors on different parts of the input space as well; however, most existing techniques only indirectly address the problem of creating such a set. We present an algorithm作者: NIP 時(shí)間: 2025-3-26 15:31 作者: 支形吊燈 時(shí)間: 2025-3-26 17:00 作者: Asperity 時(shí)間: 2025-3-26 22:45 作者: 嘴唇可修剪 時(shí)間: 2025-3-27 04:31
A Comparison of Visual Cue Combination Models, three models of visual cue combination: a weak fusion model, a modified weak fusion model, and a strong fusion model. Their relative strengths and weaknesses are evaluated on the basis of their performances on the tasks of judging the depth and shape of an ellipse. The models differ in the amount o作者: Venules 時(shí)間: 2025-3-27 08:58 作者: Recessive 時(shí)間: 2025-3-27 13:22
Self-Organised Modular Neural Networks for Encoding Data, to illustrate this is encoding high-dimensional data, such as images, where multiple network modules implement a factorial encoder, in which the high-dimensional data space is broken up into a number of lowdimensional subspaces, each of which is separately encoded. This type of factorial encoder em作者: Insul島 時(shí)間: 2025-3-27 13:48 作者: attenuate 時(shí)間: 2025-3-27 19:59 作者: 文件夾 時(shí)間: 2025-3-28 00:18 作者: conformity 時(shí)間: 2025-3-28 04:25 作者: Irremediable 時(shí)間: 2025-3-28 09:39 作者: Intercept 時(shí)間: 2025-3-28 11:23
,Lieferantenn?he: S?R Rusche GmbH,enerate the ensemble, the most common approach is through perturbations of the training set and construction of the same algorithm (trees, neural nets, etc.) using the perturbed training sets. But other methods of generating ensembles have also been explored. Combination is achieved by averaging the作者: dagger 時(shí)間: 2025-3-28 18:14 作者: 字的誤用 時(shí)間: 2025-3-28 18:44 作者: entitle 時(shí)間: 2025-3-29 00:02
Prinz Alwaleed bin Talal – der Anleger-Prinzrm what is often referred to as a neural network ensemble, may yield better model accuracy without requiring extensive efforts in training the individual networks or optimising their architecture [21, 48]. However, because the corresponding outputs of the individual networks approximate the same phy作者: 羊齒 時(shí)間: 2025-3-29 05:25 作者: abnegate 時(shí)間: 2025-3-29 10:31
Funktionsweise des Verdauungstrakts,tatistical methods such as generalized additive models. It is shown that noisy bootstrap performs best in conjunction with weight decay regularisation and ensemble averaging. The two-spiral problem, a highly nonlinear noise-free data, is used to demonstrate these findings.作者: mendacity 時(shí)間: 2025-3-29 11:25
https://doi.org/10.1007/978-3-662-59775-0 three models of visual cue combination: a weak fusion model, a modified weak fusion model, and a strong fusion model. Their relative strengths and weaknesses are evaluated on the basis of their performances on the tasks of judging the depth and shape of an ellipse. The models differ in the amount o作者: Embolic-Stroke 時(shí)間: 2025-3-29 17:32 作者: allergy 時(shí)間: 2025-3-29 22:59
https://doi.org/10.1007/978-3-658-21936-9 to illustrate this is encoding high-dimensional data, such as images, where multiple network modules implement a factorial encoder, in which the high-dimensional data space is broken up into a number of lowdimensional subspaces, each of which is separately encoded. This type of factorial encoder em作者: Jejune 時(shí)間: 2025-3-30 02:44
