標(biāo)題: Titlebook: Artificial Neural Networks and Machine Learning - ICANN 2011; 21st International C Timo Honkela,W?odzis?aw Duch,Samuel Kaski Conference pro [打印本頁(yè)] 作者: MIFF 時(shí)間: 2025-3-21 18:28
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning - ICANN 2011影響因子(影響力)
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning - ICANN 2011影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning - ICANN 2011網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning - ICANN 2011網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning - ICANN 2011被引頻次
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning - ICANN 2011被引頻次學(xué)科排名
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning - ICANN 2011年度引用
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning - ICANN 2011年度引用學(xué)科排名
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning - ICANN 2011讀者反饋
書(shū)目名稱(chēng)Artificial Neural Networks and Machine Learning - ICANN 2011讀者反饋學(xué)科排名
作者: BUCK 時(shí)間: 2025-3-21 22:43
Observational Learning Based on Models of Overlapping Pathways,ted during observation, if the agent is able to perform action association, i.e. relate its own actions with the ones of the demonstrator. In addition, by designing the model to activate the same neural codes during execution and observation, we show how the agent can accomplish observational learning of novel objects.作者: 法律 時(shí)間: 2025-3-22 02:49 作者: 頂點(diǎn) 時(shí)間: 2025-3-22 05:28
A Comparison of the Electric Potential through the Membranes of Ganglion Neurons and Neuroblastoma s, represents a pathologic neuron. We numerically solved the non-linear Poisson-Boltzmann equation, by considering the densities of charges dissolved in an electrolytic solution and fixed on both glycocalyx and cytoplasmic proteins. We found important differences among the potential profiles of the two cells.作者: PANT 時(shí)間: 2025-3-22 09:07
,Momentum Acceleration of Least–Squares Support Vector Machines,o combine the popular Sequential Minimal Optimization (SMO) method with a momentum strategy that manages to reduce the number of iterations required for convergence, while requiring little additional computational effort per iteration, especially in those situations where the standard SMO algorithm for LS–SVMs fails to obtain fast solutions.作者: Forsake 時(shí)間: 2025-3-22 15:09
,Fast Support Vector Training by Newton’s Method,ental Cholesky factorization in calculating corrections. By computer experiments, we show that the proposed method is comparable to or faster than SMO (Sequential minimum optimization) using the second order information.作者: Engaged 時(shí)間: 2025-3-22 18:49
Conference proceedings 2011 ICANN 2011, held in Espoo, Finland, in June 2011. .The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and作者: Endoscope 時(shí)間: 2025-3-23 00:26 作者: tattle 時(shí)間: 2025-3-23 02:55
https://doi.org/10.1007/978-3-7091-8634-3dom receptive fields in the image space. These . (IRF-NN) show remarkable performances for recognition applications, with extremely fast learning, and can be applied directly to images without pre-processing.作者: corporate 時(shí)間: 2025-3-23 08:45 作者: 逢迎白雪 時(shí)間: 2025-3-23 10:43
Hybrid Parallel Classifiers for Semantic Subspace Learning,are the performance of our hybrid architecture with a single classifier and show that it outperforms the single classifier system by a large margin when tested with a variety of hybrid combinations. Our results show that subspace classification accuracy is boosted and learning time reduced significantly with this new hybrid architecture.作者: 節(jié)省 時(shí)間: 2025-3-23 15:11
Image Receptive Fields Neural Networks for Object Recognition,dom receptive fields in the image space. These . (IRF-NN) show remarkable performances for recognition applications, with extremely fast learning, and can be applied directly to images without pre-processing.作者: 不能平靜 時(shí)間: 2025-3-23 21:45 作者: Cervical-Spine 時(shí)間: 2025-3-23 23:51 作者: 全部 時(shí)間: 2025-3-24 04:07 作者: 小畫(huà)像 時(shí)間: 2025-3-24 10:19 作者: 使無(wú)效 時(shí)間: 2025-3-24 13:43
The Local and Modular Fermat Problem,of collective activity. This is performed by adopting a relatively complex dynamic synaptic model. Some light is shed on the relevance of the usage of the developed framework to mimic complex cortical functions, e.g. content-addressable memory.作者: 可轉(zhuǎn)變 時(shí)間: 2025-3-24 17:59
https://doi.org/10.1007/978-3-7091-8634-3s, represents a pathologic neuron. We numerically solved the non-linear Poisson-Boltzmann equation, by considering the densities of charges dissolved in an electrolytic solution and fixed on both glycocalyx and cytoplasmic proteins. We found important differences among the potential profiles of the two cells.作者: progestin 時(shí)間: 2025-3-24 22:25 作者: 只有 時(shí)間: 2025-3-25 00:27
Alcoholic beverage fermentations,ental Cholesky factorization in calculating corrections. By computer experiments, we show that the proposed method is comparable to or faster than SMO (Sequential minimum optimization) using the second order information.作者: Adjourn 時(shí)間: 2025-3-25 05:21 作者: 火車(chē)車(chē)輪 時(shí)間: 2025-3-25 09:58
https://doi.org/10.1007/978-3-662-31589-7ifier we can additionally apply the Particle Swarm Optimization algorithm to tune its free parameters. Our experimental results show that by applying Particle Swarm Optimization on the Sub-class Linear Discriminant Error Correcting Output Codes framework we get a significant improvement in the classification performance.作者: CODA 時(shí)間: 2025-3-25 15:04 作者: 交響樂(lè) 時(shí)間: 2025-3-25 17:56
Optimizing Linear Discriminant Error Correcting Output Codes Using Particle Swarm Optimization,ifier we can additionally apply the Particle Swarm Optimization algorithm to tune its free parameters. Our experimental results show that by applying Particle Swarm Optimization on the Sub-class Linear Discriminant Error Correcting Output Codes framework we get a significant improvement in the classification performance.作者: fallible 時(shí)間: 2025-3-25 22:56 作者: exquisite 時(shí)間: 2025-3-26 01:59 作者: arbovirus 時(shí)間: 2025-3-26 06:25
Fermat’s Last Theorem for Amateursn provides a fast adjustment of the BCI system to mild changes of the signal. The proposed algorithm was validated on artificial and real data sets. In comparison to generic Multi-Way PLS, the recursive algorithm demonstrates good performance and robustness.作者: flaunt 時(shí)間: 2025-3-26 11:41
Fermat’s Last Theorem for Amateursed for regression problems of big and complex datasets. It was applied to the problem of steel temperature prediction in the electric arc furnace in order to decrease the process duration at one of the steelworks.作者: sebaceous-gland 時(shí)間: 2025-3-26 15:25 作者: Little 時(shí)間: 2025-3-26 17:54
Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs,very weak supervision. The model generates plausible samples and performs foreground-background segmentation. We demonstrate that representing foreground objects independently of the background can be beneficial in recognition tasks.作者: 結(jié)束 時(shí)間: 2025-3-26 23:24
Recursive Multi-Way PLS for Adaptive Calibration of Brain Computer Interface System,n provides a fast adjustment of the BCI system to mild changes of the signal. The proposed algorithm was validated on artificial and real data sets. In comparison to generic Multi-Way PLS, the recursive algorithm demonstrates good performance and robustness.作者: Stricture 時(shí)間: 2025-3-27 01:56 作者: 一瞥 時(shí)間: 2025-3-27 09:06 作者: 缺乏 時(shí)間: 2025-3-27 09:57 作者: antiquated 時(shí)間: 2025-3-27 14:25
Fermat’s Last Theorem for Amateurssponse functions were estimated from neuroimaging data acquired while a subject was watching checkerboard patterns and geometrical figures. Furthermore, we demonstrate that reconstructions of the original stimuli can be generated by loopy belief propagation in a Markov random field.作者: Incommensurate 時(shí)間: 2025-3-27 21:24
Fermat’s Last Theorem for Amateurs and each agent is implemented as an independent element that follows its own behavioral model which is composed of four steering behaviors: ., ., . and .. The synchronized motion of the flock emerges from combination of those behaviors. The control design will be discussed in theoretical terms, supported by simulation results.作者: 摻和 時(shí)間: 2025-3-28 01:36 作者: Gullible 時(shí)間: 2025-3-28 03:37
Timo Honkela,W?odzis?aw Duch,Samuel KaskiFast track conference proceedings.Unique visibility.State of the art research作者: 暫時(shí)休息 時(shí)間: 2025-3-28 09:21 作者: 半身雕像 時(shí)間: 2025-3-28 13:48 作者: 要控制 時(shí)間: 2025-3-28 17:06
A Distributed Behavioral Model Using Neural Fields, and each agent is implemented as an independent element that follows its own behavioral model which is composed of four steering behaviors: ., ., . and .. The synchronized motion of the flock emerges from combination of those behaviors. The control design will be discussed in theoretical terms, supported by simulation results.作者: 貨物 時(shí)間: 2025-3-28 18:54
Binary Patterns Identification by Vector Neural Network with Measure of Proximity between Neuron Stcount the noise distribution allows to essentially increase the system noise immunity. A measure of proximity between neuron states is embedded for the first time. It makes possible to use the prior information. On binary identification problem the one order increase of storage capacity is shown.作者: opportune 時(shí)間: 2025-3-28 22:54
Conference proceedings 2011 ICANN 2011, held in Espoo, Finland, in June 2011. .The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and applications.作者: Tempor 時(shí)間: 2025-3-29 06:11
Fermat’s Last Theorem for Amateurssponse functions were estimated from neuroimaging data acquired while a subject was watching checkerboard patterns and geometrical figures. Furthermore, we demonstrate that reconstructions of the original stimuli can be generated by loopy belief propagation in a Markov random field.作者: 名詞 時(shí)間: 2025-3-29 08:05
Reformulations, Consequences, and Criteria, to be modeled independently of the background. We present a learning scheme that learns this representation directly from cluttered images with only very weak supervision. The model generates plausible samples and performs foreground-background segmentation. We demonstrate that representing foregro作者: 多產(chǎn)子 時(shí)間: 2025-3-29 12:13
Fermat’s Last Theorem for Amateursition with a scheme of recursive calculation. This Recursive algorithm allows treating data arrays of huge dimension. In addition, adaptive calibration provides a fast adjustment of the BCI system to mild changes of the signal. The proposed algorithm was validated on artificial and real data sets. I作者: 史前 時(shí)間: 2025-3-29 15:45 作者: Stable-Angina 時(shí)間: 2025-3-29 22:20
Fermat’s Last Theorem for Amateurs and each agent is implemented as an independent element that follows its own behavioral model which is composed of four steering behaviors: ., ., . and .. The synchronized motion of the flock emerges from combination of those behaviors. The control design will be discussed in theoretical terms, sup作者: FLINT 時(shí)間: 2025-3-29 23:53
Fermat’s Last Theorem for Amateursat are conceptually similar to the Hopfield ones. We show that using this approach a synapse exposes stable operating points in terms of its excitatory postsynaptic potential (EPSP) as a function of its synaptic strength. We postulate that synapses in a network operating at these stable points can d作者: Conflict 時(shí)間: 2025-3-30 06:35
Fermat’s Last Theorem for Amateursegions in the parietal, primary motor and somatosensory lobes. In the present paper we consider how learning via observation can be implemented in an artificial agent based on the above overlapping pathway of activations. We demonstrate that the circuitry developed for action execution can be activa作者: 領(lǐng)帶 時(shí)間: 2025-3-30 11:45 作者: ENDOW 時(shí)間: 2025-3-30 15:21 作者: NICHE 時(shí)間: 2025-3-30 18:29
Fermat’s Last Theorem for Amateurs. The decision tree consists of MLP neural networks, which optimize the split points and at the leaf level predict final outputs. The system is designed for regression problems of big and complex datasets. It was applied to the problem of steel temperature prediction in the electric arc furnace in o作者: 煩憂 時(shí)間: 2025-3-30 21:33
https://doi.org/10.1007/978-3-662-31589-7lass problem is decomposed into several binary ones. On these created sub-problems we apply binary classifiers and then, by combining the acquired solutions, we are able to solve the initial multi-class problem. In this paper we consider the optimization of the Linear Discriminant Error Correcting O作者: 敵意 時(shí)間: 2025-3-31 01:58
Charakterisierung von Bioreaktoren, Self Organizing Structure of HMM) allows to learn the Hidden Markov Models topology. The main contribution for the proposed approach is to automatically extract the structure of a hidden Markov model without any prior knowledge of the application domain. This model can be represented as a graph of 作者: 個(gè)人長(zhǎng)篇演說(shuō) 時(shí)間: 2025-3-31 08:01 作者: 猜忌 時(shí)間: 2025-3-31 12:13
https://doi.org/10.1007/978-3-7091-8634-3ion potential states, and analyzed the influence of fixed charges of the membrane on the electric potential of the surface of the membranes of these cells, based on experimental values of membrane properties. The ganglion neuron portrays a healthy neuron, and the neuroblastoma cell, which is tumorou作者: Culpable 時(shí)間: 2025-3-31 15:43 作者: Callus 時(shí)間: 2025-3-31 18:00 作者: 擁護(hù) 時(shí)間: 2025-4-1 00:40 作者: 微塵 時(shí)間: 2025-4-1 01:58
Andrew G. H. Lea,Jean-Fran?ois DrilleauMs), and used in a wide range of applications. In spite of this, only a limited effort has been realized to design efficient algorithms for the training of this class of models, in clear contrast to the vast amount of contributions of this kind in the field of classic SVMs. In this work we propose t作者: 開(kāi)始發(fā)作 時(shí)間: 2025-4-1 08:37 作者: Mystic 時(shí)間: 2025-4-1 14:01 作者: obeisance 時(shí)間: 2025-4-1 14:26
978-3-642-21737-1Springer-Verlag GmbH Berlin Heidelberg 2011作者: 單片眼鏡 時(shí)間: 2025-4-1 21:09
