作者: Factual 時間: 2025-3-21 21:53 作者: Vulvodynia 時間: 2025-3-22 03:33
Improving the Prediction Accuracy of Echo State Neural Networks by Anti-Oja’s Learningal to achieve their greater prediction ability. A standard training of these neural networks uses pseudoinverse matrix for one-step learning of weights from hidden to output neurons. This regular adaptation of Echo State neural networks was optimized by updating the weights of the dynamic reservoir 作者: 自由職業(yè)者 時間: 2025-3-22 04:55
Theoretical Analysis of Accuracy of Gaussian Belief Propagationwn to provide true marginal probabilities when the graph describing the target distribution has a tree structure, while do approximate marginal probabilities when the graph has loops. The accuracy of loopy belief propagation (LBP) has been studied. In this paper, we focus on applying LBP to a multi-作者: Flagging 時間: 2025-3-22 10:42
Relevance Metrics to Reduce Input Dimensions in Artificial Neural Networks inputs is desirable in order to obtain better generalisation capabilities with the models. There are several approaches to perform input selection. In this work we will deal with techniques guided by measures of input relevance or input sensitivity. Six strategies to assess input relevance were tes作者: 許可 時間: 2025-3-22 16:31
An Improved Greedy Bayesian Network Learning Algorithm on Limited Dataor information theoretical measure or a score function may be unreliable on limited datasets, which affects learning accuracy. To alleviate the above problem, we propose a novel BN learning algorithm MRMRG, Max Relevance and Min Redundancy Greedy algorithm. MRMRG algorithm applies Max Relevance and 作者: Directed 時間: 2025-3-22 20:50
Incremental One-Class Learning with Bounded Computational Complexity - the probability distribution of the training data. In the early stages of training a non-parametric estimate of the training data distribution is obtained using kernel density estimation. Once the number of training examples reaches the maximum computationally feasible limit for kernel density es作者: 赤字 時間: 2025-3-23 00:49
Estimating the Size of Neural Networks from the Number of Available Training Datads on the size of neural networks that are unrealistic to implement. This work provides a computational study for estimating the size of neural networks using as an estimation parameter the size of available training data. We will also show that the size of a neural network is problem dependent and 作者: Confess 時間: 2025-3-23 03:05 作者: hangdog 時間: 2025-3-23 08:08 作者: jocular 時間: 2025-3-23 13:27 作者: 大氣層 時間: 2025-3-23 14:04 作者: garrulous 時間: 2025-3-23 18:21 作者: RECUR 時間: 2025-3-23 23:19
Recurrent Bayesian Reasoning in Probabilistic Neural Networksonal probability distributions by finite mixtures of product components. The mixture components can be interpreted as probabilistic neurons in neurophysiological terms and, in this respect, the fixed probabilistic description becomes conflicting with the well known short-term dynamic properties of b作者: pacific 時間: 2025-3-24 05:14
Resilient Approximation of Kernel Classifiersge. Approximating the SVM by a more sparse function has been proposed to solve to this problem. In this study, different variants of approximation algorithms are empirically compared. It is shown that gradient descent using the improved Rprop algorithm increases the robustness of the method compared作者: Lineage 時間: 2025-3-24 08:25
Incremental Learning of Spatio-temporal Patterns with Model Selectioning through sleep (ILS)” method. This method alternately repeats two learning phases: awake and sleep. During the awake phase, the system learns new spatio-temporal patterns by rote, whereas in the sleep phase, it rehearses the recorded new memories interleaved with old memories. The rehearsal proce作者: 連接 時間: 2025-3-24 11:00 作者: ETCH 時間: 2025-3-24 15:42
Analysis and Comparative Study of Source Separation Performances in Feed-Forward and Feed-Back BSSs m in the solution space, and signal distortion is likely to occur in convolutive mixtures. On the other hand, the FB-BSS structure does not cause signal distortion. However, it requires a condition on the propagation delays in the mixing process. In this paper, source separation performance in the F作者: Exposure 時間: 2025-3-24 22:35 作者: epinephrine 時間: 2025-3-25 00:28
Joaquim Marques Sá,Luís A. Alexandre,Danilo Mandic作者: Watemelon 時間: 2025-3-25 04:49
https://doi.org/10.1007/978-3-540-74690-4Boolean function; algorithmic learning; algorithms; bioinspired computing; biomedical data analysis; clas作者: FLIT 時間: 2025-3-25 10:50
978-3-540-74689-8Springer-Verlag Berlin Heidelberg 2007作者: GEON 時間: 2025-3-25 15:21 作者: HAIRY 時間: 2025-3-25 17:27 作者: liposuction 時間: 2025-3-25 22:01
Gang der Versuchsdurchrechnungen,al to achieve their greater prediction ability. A standard training of these neural networks uses pseudoinverse matrix for one-step learning of weights from hidden to output neurons. This regular adaptation of Echo State neural networks was optimized by updating the weights of the dynamic reservoir 作者: 悲觀 時間: 2025-3-26 03:42 作者: 釘牢 時間: 2025-3-26 05:54 作者: 生命層 時間: 2025-3-26 10:11 作者: 教義 時間: 2025-3-26 15:28 作者: scrutiny 時間: 2025-3-26 20:46
Versuche mit gasreichen Kohlen,ds on the size of neural networks that are unrealistic to implement. This work provides a computational study for estimating the size of neural networks using as an estimation parameter the size of available training data. We will also show that the size of a neural network is problem dependent and 作者: 煩擾 時間: 2025-3-26 21:25 作者: 管理員 時間: 2025-3-27 04:58 作者: Ringworm 時間: 2025-3-27 07:52
https://doi.org/10.1007/978-3-476-03026-9erefore depends on well diversity among component NNs. Popular NNE methods, such as bagging and boosting, follow data sampling technique to achieve diversity. In such methods, NN is trained independently with a particular training set that is probabilistically created. Due to independent training st作者: 蕁麻 時間: 2025-3-27 09:49
Vidya Sagar Bhasin,Indranil Mazumdarsidual and to facilitate the regression character of NRR, we incorporate an improved, Auxiliared Bellman Residual [2] and provide, to the best of our knowledge, the first Neural Network based implementation of the novel Bellman Residual minimisation technique. Furthermore, we extend NRR to Policy Gr作者: innovation 時間: 2025-3-27 13:37 作者: 審問 時間: 2025-3-27 20:53
,3He(e,e′p) : A proposed experiment,onal probability distributions by finite mixtures of product components. The mixture components can be interpreted as probabilistic neurons in neurophysiological terms and, in this respect, the fixed probabilistic description becomes conflicting with the well known short-term dynamic properties of b作者: 吼叫 時間: 2025-3-27 23:54
https://doi.org/10.1007/3-540-09095-9ge. Approximating the SVM by a more sparse function has been proposed to solve to this problem. In this study, different variants of approximation algorithms are empirically compared. It is shown that gradient descent using the improved Rprop algorithm increases the robustness of the method compared作者: Confidential 時間: 2025-3-28 02:58
https://doi.org/10.1007/3-540-09095-9ing through sleep (ILS)” method. This method alternately repeats two learning phases: awake and sleep. During the awake phase, the system learns new spatio-temporal patterns by rote, whereas in the sleep phase, it rehearses the recorded new memories interleaved with old memories. The rehearsal proce作者: 喊叫 時間: 2025-3-28 08:02 作者: interlude 時間: 2025-3-28 11:59
On exchange currents in electron scattering,m in the solution space, and signal distortion is likely to occur in convolutive mixtures. On the other hand, the FB-BSS structure does not cause signal distortion. However, it requires a condition on the propagation delays in the mixing process. In this paper, source separation performance in the F作者: 污穢 時間: 2025-3-28 14:54 作者: 單獨 時間: 2025-3-28 21:03
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162694.jpg作者: GIDDY 時間: 2025-3-29 00:54 作者: Colonoscopy 時間: 2025-3-29 03:46
Artificial Neural Networks - ICANN 2007978-3-540-74690-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 是比賽 時間: 2025-3-29 08:02
Erratum to: Versuche mit gasreichen Kohlen,earning performance from the regular statistical models. In this paper, we show that the learning coefficient is easily computed by weighted blow up, in contrast, and that there is the case that the learning coefficient cannot be correctly computed by blowing up at the origin . only.作者: invulnerable 時間: 2025-3-29 12:58 作者: penance 時間: 2025-3-29 17:28 作者: 變白 時間: 2025-3-29 22:58
,3He(e,e′p) : A proposed experiment,iological neurons. We show that some parameters of PNN can be “released” for the sake of dynamic processes without destroying the statistically correct decision making. In particular, we can iteratively adapt the mixture component weights or modify the input pattern in order to facilitate the correct recognition.作者: Formidable 時間: 2025-3-30 00:40
https://doi.org/10.1007/3-540-09095-9 to fixed-point iteration. Three different heuristics for selecting the support vectors to be used in the construction of the sparse approximation are proposed. It turns out that none is superior to random selection. The effect of a finishing gradient descent on all parameters of the sparse approximation is studied.作者: 溝通 時間: 2025-3-30 07:42
,3He(e,e′p) : A proposed experiment,n such problems with quite good results. The computational cost of training is low because most nodes and connections are fixed and only weights of one node are modified at each training step. Several examples of learning Boolean functions and results of classification tests on real-world multiclass datasets are presented.作者: 發(fā)出眩目光芒 時間: 2025-3-30 11:49 作者: 空氣 時間: 2025-3-30 14:23 作者: arthroscopy 時間: 2025-3-30 18:56
Recurrent Bayesian Reasoning in Probabilistic Neural Networksiological neurons. We show that some parameters of PNN can be “released” for the sake of dynamic processes without destroying the statistically correct decision making. In particular, we can iteratively adapt the mixture component weights or modify the input pattern in order to facilitate the correct recognition.作者: 變形 時間: 2025-3-30 21:55 作者: 落葉劑 時間: 2025-3-31 01:57
Learning Highly Non-separable Boolean Functions Using Constructive Feedforward Neural Networkn such problems with quite good results. The computational cost of training is low because most nodes and connections are fixed and only weights of one node are modified at each training step. Several examples of learning Boolean functions and results of classification tests on real-world multiclass datasets are presented.作者: 織物 時間: 2025-3-31 05:34
Conference proceedings 2007ICANN 2007, held in Porto, Portugal, in September 2007...The 197 revised full papers presented were carefully reviewed and selected from 376 submissions. The 98 papers of the first volume are organized in topical sections on learning theory, advances in neural network learning methods, ensemble lear作者: 遭受 時間: 2025-3-31 11:03
0302-9743 Networks, ICANN 2007, held in Porto, Portugal, in September 2007...The 197 revised full papers presented were carefully reviewed and selected from 376 submissions. The 98 papers of the first volume are organized in topical sections on learning theory, advances in neural network learning methods, ens作者: 樹膠 時間: 2025-3-31 13:41 作者: faultfinder 時間: 2025-3-31 20:05
https://doi.org/10.1007/978-3-642-91647-2gnificance of each component in the mixture, and the other one is to discriminate the relevance of each feature to the cluster structure. The experiments on both the synthetic and real-world data show the efficacy of the proposed algorithm.作者: libertine 時間: 2025-3-31 23:58
Vidya Sagar Bhasin,Indranil Mazumdaranteed. Model selection is based on predictive assessment, with efficient algorithms that allow fast greedy forward and backward selection within the class of decomposable models. We show the validity of this structure learning approach on toy data, and on two large sets of gene expression data.
