書目名稱Artificial Neural Networks in Pattern Recognition影響因子(影響力)學(xué)科排名
書目名稱Artificial Neural Networks in Pattern Recognition網(wǎng)絡(luò)公開度
書目名稱Artificial Neural Networks in Pattern Recognition網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Neural Networks in Pattern Recognition被引頻次
書目名稱Artificial Neural Networks in Pattern Recognition被引頻次學(xué)科排名
書目名稱Artificial Neural Networks in Pattern Recognition年度引用
書目名稱Artificial Neural Networks in Pattern Recognition年度引用學(xué)科排名
書目名稱Artificial Neural Networks in Pattern Recognition讀者反饋
書目名稱Artificial Neural Networks in Pattern Recognition讀者反饋學(xué)科排名
作者: 頑固 時(shí)間: 2025-3-21 23:46
,Die Gyn?kologie des Truppenarztes,ty of the problem to face, linear dynamical systems may directly contribute to provide a good solution at a reduced computational cost, or indirectly provide support at a pre-training stage for nonlinear models. We present and discuss several approaches, both linear and nonlinear, where linear dynam作者: Affable 時(shí)間: 2025-3-22 03:18
https://doi.org/10.1007/978-3-662-34565-8e usually inferior to support vector machines (SVMs). To improve the generalization abilities of MAMCs, in this paper, we propose optimizing slopes and bias terms of separating hyperplanes after the coefficient vectors of the hyperplanes are obtained. The bias term is optimized so that the number of作者: 摸索 時(shí)間: 2025-3-22 07:37 作者: Charade 時(shí)間: 2025-3-22 09:58 作者: ARENA 時(shí)間: 2025-3-22 16:50 作者: UNT 時(shí)間: 2025-3-22 18:58 作者: 撤退 時(shí)間: 2025-3-22 21:47 作者: Misnomer 時(shí)間: 2025-3-23 03:53 作者: 為現(xiàn)場(chǎng) 時(shí)間: 2025-3-23 06:50 作者: Sciatica 時(shí)間: 2025-3-23 13:11 作者: 委托 時(shí)間: 2025-3-23 15:21 作者: Medicare 時(shí)間: 2025-3-23 21:19 作者: 愛管閑事 時(shí)間: 2025-3-23 23:58
https://doi.org/10.1007/978-3-658-37822-6l only equipped with standard CPU systems. In this paper, we investigate multiple techniques to speedup the training of Restricted Boltzmann Machine (RBM) models and Convolutional RBM (CRBM) models on CPU with the Contrastive Divergence (CD) algorithm. Experimentally, we show that the proposed techn作者: albuminuria 時(shí)間: 2025-3-24 03:31 作者: Frequency-Range 時(shí)間: 2025-3-24 09:37 作者: 除草劑 時(shí)間: 2025-3-24 12:02 作者: 壓倒性勝利 時(shí)間: 2025-3-24 17:46
978-3-319-46181-6Springer Nature Switzerland AG 2016作者: 紋章 時(shí)間: 2025-3-24 20:24
https://doi.org/10.1007/978-3-662-34565-8EGG records the resultant body surface potential of gastric slow waves (electrical activity); while slow waves regulate contractions of gastric muscles, it is the electrical activity we are recording, not movement (like ECG records the cardiac electrical activity, but not the contractions of the heart, even the two are essentially related).作者: VEN 時(shí)間: 2025-3-24 23:56
A Spiking Neural Network for Personalised Modelling of Electrogastrography (EGG)EGG records the resultant body surface potential of gastric slow waves (electrical activity); while slow waves regulate contractions of gastric muscles, it is the electrical activity we are recording, not movement (like ECG records the cardiac electrical activity, but not the contractions of the heart, even the two are essentially related).作者: Optic-Disk 時(shí)間: 2025-3-25 06:27 作者: senile-dementia 時(shí)間: 2025-3-25 10:11 作者: confederacy 時(shí)間: 2025-3-25 14:02
Learning Sequential Data with the Help of Linear Systemsty of the problem to face, linear dynamical systems may directly contribute to provide a good solution at a reduced computational cost, or indirectly provide support at a pre-training stage for nonlinear models. We present and discuss several approaches, both linear and nonlinear, where linear dynam作者: 你不公正 時(shí)間: 2025-3-25 18:06 作者: 酷熱 時(shí)間: 2025-3-25 21:37 作者: chlorosis 時(shí)間: 2025-3-26 00:40
Incremental Construction of Low-Dimensional Data Representationsperties of the initial data. Typically, such algorithms use the solution of large-dimensional optimization problems, and the incremental versions are designed for many popular algorithms to reduce their computational complexity. Under manifold assumption about high-dimensional data, advanced manifol作者: dyspareunia 時(shí)間: 2025-3-26 06:40 作者: indicate 時(shí)間: 2025-3-26 12:25 作者: AROMA 時(shí)間: 2025-3-26 13:26
Co-training with Credal Models convex probability sets, they can select multiple classes as prediction when information is insufficient and predict a unique class only when the available information is rich enough. The goal of this paper is to explore whether this particular feature can be used advantageously in the setting of c作者: OUTRE 時(shí)間: 2025-3-26 20:51
Interpretable Classifiers in Precision Medicine: Feature Selection and Multi-class Categorizationnd treatments this change also alters the diagnostic task from binary to multi-categorial decisions. Keeping the corresponding multi-class architectures accurate and interpretable is currently one of the key tasks in molecular diagnostics..In this work, we specifically address the question to which 作者: Insubordinate 時(shí)間: 2025-3-26 21:39 作者: 向宇宙 時(shí)間: 2025-3-27 04:01
On the Harmony Search Using Quaternionsnsional spaces, non-convex functions might become too tricky to be optimized, thus requiring different representations aiming at smoother fitness landscapes. In this paper, we present a variant of the Harmony Search algorithm based on quaternions, which extend complex numbers and have been shown to 作者: DEMUR 時(shí)間: 2025-3-27 08:56 作者: 收養(yǎng) 時(shí)間: 2025-3-27 10:41
Towards Effective Classification of Imbalanced Data with Convolutional Neural Networksl network classifiers fail to learn to classify such datasets correctly if class-to-class separability is poor due to a strong bias towards the majority class. In this paper we present an algorithmic solution, integrating different methods into a novel approach using a class-to-class separability sc作者: BUST 時(shí)間: 2025-3-27 17:15 作者: 小卒 時(shí)間: 2025-3-27 19:36
Comparing Incremental Learning Strategies for Convolutional Neural Networksn and object detection, being able to extract meaningful high-level invariant features. However, partly because of their complex training and tricky hyper-parameters tuning, CNNs have been scarcely studied in the context of incremental learning where data are available in consecutive batches and ret作者: Microaneurysm 時(shí)間: 2025-3-27 22:07
Approximation of Graph Edit Distance by Means of a Utility Matrixf a linear sum assignment problem, the major drawback of this dissimilarity model, viz.?the exponential time complexity, has been invalidated recently. Yet, the substantial decrease of the computation time is at the expense of an approximation error. The present paper introduces a novel transformati作者: anchor 時(shí)間: 2025-3-28 05:43
Learning Sequential Data with the Help of Linear Systemsical systems play an important role. These approaches are empirically assessed on two nontrivial datasets of sequences on a prediction task. Experimental results show that indeed linear dynamical systems can either directly provide a satisfactory solution, as well as they may be crucial for the success of more sophisticated nonlinear approaches.作者: 虛假 時(shí)間: 2025-3-28 09:05
Co-training with Credal Modelso-training, in which a classifier strengthen another one by feeding it with new labeled data. We propose several co-training strategies to exploit the potential indeterminacy of credal classifiers and test them on several UCI datasets. We then compare the best strategy to the standard co-training process to check its efficiency.作者: 后來(lái) 時(shí)間: 2025-3-28 12:56
Interpretable Classifiers in Precision Medicine: Feature Selection and Multi-class Categorizationextent biomarkers that characterize pairwise differences among classes, correspond to biomarkers that discriminate one class from all remaining. We compare one-against-one and one-against-all architectures of feature selecting base classifiers. They are validated for their classification performance and their stability of feature selection.作者: 蓋他為秘密 時(shí)間: 2025-3-28 14:55 作者: LARK 時(shí)間: 2025-3-28 21:42
0302-9743 s working in all areas of neural network- and machine learning-based pattern recognition to present and discuss the latest research, results, and ideas in these areas...?.978-3-319-46181-6978-3-319-46182-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Fulminate 時(shí)間: 2025-3-29 00:49 作者: AWE 時(shí)間: 2025-3-29 03:57 作者: bonnet 時(shí)間: 2025-3-29 10:31
https://doi.org/10.1007/978-3-662-25786-9yper-parameters tuning, CNNs have been scarcely studied in the context of incremental learning where data are available in consecutive batches and retraining the model from scratch is unfeasible. In this work we compare different incremental learning strategies for CNN based architectures, targeting real-word applications.作者: GROVE 時(shí)間: 2025-3-29 14:58
On CPU Performance Optimization of Restricted Boltzmann Machine and Convolutional RBMRBM) models and Convolutional RBM (CRBM) models on CPU with the Contrastive Divergence (CD) algorithm. Experimentally, we show that the proposed techniques can reduce the training time by up?to 30 times for RBM and up?to 12 times for CRBM, on a data set of handwritten digits.作者: Arboreal 時(shí)間: 2025-3-29 15:39 作者: 荒唐 時(shí)間: 2025-3-29 22:28
Conference proceedings 2016cted from 32 submissions for inclusion in this volume. The workshop will act as a major forum for international researchers and practitioners working in all areas of neural network- and machine learning-based pattern recognition to present and discuss the latest research, results, and ideas in these areas...?.作者: 無(wú)辜 時(shí)間: 2025-3-30 00:53
,Die Gyn?kologie des Truppenarztes,ical systems play an important role. These approaches are empirically assessed on two nontrivial datasets of sequences on a prediction task. Experimental results show that indeed linear dynamical systems can either directly provide a satisfactory solution, as well as they may be crucial for the success of more sophisticated nonlinear approaches.作者: 雄辯 時(shí)間: 2025-3-30 06:10
Festschrift zum 70. Geburtstageo-training, in which a classifier strengthen another one by feeding it with new labeled data. We propose several co-training strategies to exploit the potential indeterminacy of credal classifiers and test them on several UCI datasets. We then compare the best strategy to the standard co-training process to check its efficiency.作者: 錯(cuò)事 時(shí)間: 2025-3-30 11:22
,über erbliche Tuberkulosedisposition,extent biomarkers that characterize pairwise differences among classes, correspond to biomarkers that discriminate one class from all remaining. We compare one-against-one and one-against-all architectures of feature selecting base classifiers. They are validated for their classification performance and their stability of feature selection.作者: Synovial-Fluid 時(shí)間: 2025-3-30 13:38 作者: Manifest 時(shí)間: 2025-3-30 18:43
,über Mesaortitis und K?rperkonstitution,sfy numerically the constraint on the integral of the function learned by the MLP. The preliminary outcomes of a simulation on data drawn from a mixture of Fisher-Tippett pdfs are reported on, and compared graphically with the estimates yielded by statistical techniques, showing the viability of the approach.作者: Flatus 時(shí)間: 2025-3-30 20:58 作者: chassis 時(shí)間: 2025-3-31 04:39
