標題: Titlebook: Artificial Neural Networks in Pattern Recognition; Second IAPR Workshop Friedhelm Schwenker,Simone Marinai Conference proceedings 2006 Spri [打印本頁] 作者: opioid 時間: 2025-3-21 16:10
書目名稱Artificial Neural Networks in Pattern Recognition影響因子(影響力)
書目名稱Artificial Neural Networks in Pattern Recognition影響因子(影響力)學科排名
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讀者反饋學科排名
作者: tympanometry 時間: 2025-3-21 22:54
Comparison Between Two Spatio-Temporal Organization Maps for Speech Recognitionbased on Self-Organizing Map (SOM) yielding to a Spatio-Temporel Organization Map (STOM). More precisely, the map is trained using two different spatio-temporal algorithms taking their roots in biological researches: The ST-Kohonen and the Time-Organized Map (TOM). These algorithms use two kinds of 作者: 我沒有命令 時間: 2025-3-22 02:08 作者: Metamorphosis 時間: 2025-3-22 07:51
Supervised Batch Neural Gasto learn a (possibly fuzzy) supervised classification. Here we propose a batch version for supervised neural gas training which allows to efficiently learn a prototype-based classification, provided training data are given beforehand. The method relies on a simpler cost function than online supervis作者: heterodox 時間: 2025-3-22 08:45 作者: Guaff豪情痛飲 時間: 2025-3-22 13:55 作者: 不法行為 時間: 2025-3-22 17:31
A Study of the Robustness of KNN Classifiers Trained Using Soft Labelsing classes exist. In this work we attempt to compare between learning using soft and hard labels to train K-nearest neighbor classifiers. We propose a new technique to generate soft labels based on fuzzy-clustering of the data and fuzzy relabelling of cluster prototypes. Experiments were conducted 作者: 糾纏 時間: 2025-3-22 22:28 作者: 撫育 時間: 2025-3-23 04:56
A Local Tangent Space Alignment Based Transductive Classification Algorithmnal coordinates of high-dimensional data, and can also reconstruct high dimensional coordinates from embedding coordinates. But it ignores the label information conveyed by data samples, and can not be used for classification directly. In this paper, a transductive manifold classification method, ca作者: Ankylo- 時間: 2025-3-23 08:14
Incremental Manifold Learning Via Tangent Space Alignment to extract the intrinsic characteristic of different type of high-dimensional data by performing nonlinear dimensionality reduction. Most of them operate in a “batch” mode and cannot be efficiently applied when data are collected sequentially. In this paper, we proposed an incremental version (ILTS作者: 審問 時間: 2025-3-23 12:05 作者: INCUR 時間: 2025-3-23 17:03 作者: GLOOM 時間: 2025-3-23 21:10
Incremental Training of Support Vector Machines Using Truncated Hyperconespercones. We generate the truncated surface with the center being the center of unbounded support vectors and with the radius being the maximum distance from the center to support vectors. We determine the hypercone surface so that it includes a datum, which is far away from the separating hyperplan作者: Mortal 時間: 2025-3-23 22:31
Fast Training of Linear Programming Support Vector Machines Using Decomposition Techniqueslementation of decomposition techniques leads to infinite loops. To solve this problem and to further speed up training, in this paper, we propose an improved decomposition techniques for training LP-SVMs. If an infinite loop is detected, we include in the next working set all the data in the workin作者: Enthralling 時間: 2025-3-24 04:45
Multiple Classifier Systems for Embedded String Patterns. However, there has been reported only little work on combining classifiers in structural pattern recognition. In this paper we describe a method for embedding strings into real vector spaces based on prototype selection, in order to gain several vectorial descriptions of the string data. We presen作者: 擁擠前 時間: 2025-3-24 07:42 作者: Palpable 時間: 2025-3-24 12:29
Hierarchical Neural Networks Utilising Dempster-Shafer Evidence Theorye used to retrieve the classification result. More complex ways of evaluating the hierarchy output that take into account the complete information the hierarchy provides yield improved classification results. Due to the hierarchical output space decomposition that is inherent to hierarchical neural 作者: Juvenile 時間: 2025-3-24 16:36
Combining MF Networks: A Comparison Among Statistical Methods and Stacked Generalizationa single output. In this paper we focus on the combination module. We have proposed two methods based on . as the combination module of an ensemble of neural networks. In this paper we have performed a comparison among the two versions of . and six statistical combination methods in order to get the作者: 轎車 時間: 2025-3-24 20:52
https://doi.org/10.1007/11829898artificial neural network; bioinformatics; cognition; data mining; learning; neural network; pattern recog作者: 令人心醉 時間: 2025-3-25 02:01
978-3-540-37951-5Springer-Verlag GmbH Germany, part of Springer Nature 2006作者: 看法等 時間: 2025-3-25 03:55
Gedanken zu der heutigen Musik,the form of the underlying, unknown distribution. Nonparametric techniques remove this assumption In particular, the Parzen Window (PW) relies on a combination of local window functions centered in the patterns of a training sample. Although effective, PW suffers from several limitations. Artificial作者: convert 時間: 2025-3-25 09:38 作者: Foregery 時間: 2025-3-25 14:35 作者: etiquette 時間: 2025-3-25 19:07 作者: 教唆 時間: 2025-3-25 23:36 作者: GLUE 時間: 2025-3-26 03:47 作者: Confidential 時間: 2025-3-26 06:26 作者: 卵石 時間: 2025-3-26 08:52 作者: ostrish 時間: 2025-3-26 12:53 作者: Extricate 時間: 2025-3-26 18:44 作者: 不再流行 時間: 2025-3-27 00:26 作者: 不能逃避 時間: 2025-3-27 03:54 作者: CORD 時間: 2025-3-27 06:11 作者: GOAD 時間: 2025-3-27 11:19
William C. Horrace,Kurt E. Schnierlementation of decomposition techniques leads to infinite loops. To solve this problem and to further speed up training, in this paper, we propose an improved decomposition techniques for training LP-SVMs. If an infinite loop is detected, we include in the next working set all the data in the workin作者: 特別容易碎 時間: 2025-3-27 16:26
Seung Chan Ahn,Hyungsik Roger Moon. However, there has been reported only little work on combining classifiers in structural pattern recognition. In this paper we describe a method for embedding strings into real vector spaces based on prototype selection, in order to gain several vectorial descriptions of the string data. We presen作者: Emasculate 時間: 2025-3-27 18:02 作者: 加強防衛(wèi) 時間: 2025-3-27 23:31
https://doi.org/10.1007/978-3-030-69009-0e used to retrieve the classification result. More complex ways of evaluating the hierarchy output that take into account the complete information the hierarchy provides yield improved classification results. Due to the hierarchical output space decomposition that is inherent to hierarchical neural 作者: IST 時間: 2025-3-28 04:07
https://doi.org/10.1007/978-3-662-28738-5a single output. In this paper we focus on the combination module. We have proposed two methods based on . as the combination module of an ensemble of neural networks. In this paper we have performed a comparison among the two versions of . and six statistical combination methods in order to get the作者: Inordinate 時間: 2025-3-28 07:09 作者: Working-Memory 時間: 2025-3-28 14:01 作者: 細菌等 時間: 2025-3-28 15:06 作者: STIT 時間: 2025-3-28 22:13
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162681.jpg作者: 劇本 時間: 2025-3-28 23:30
Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypesa robust classifier where efficient learning with fuzzy labeled or partially contradictory data is possible. On the other hand, the integration of labeling into the location of prototypes in a SOM leads to a visualization of those parts of the data relevant for the classification.作者: maroon 時間: 2025-3-29 03:38 作者: Cougar 時間: 2025-3-29 10:00
,rkl — H?rkladde für Siegfried J. Schmidt,hen the kernel parameter is optimized. According to the computer experiments for four benchmark problems, estimation performance of a Mahalanobis kernel with a diagonal covariance matrix optimized by line search is comparable to or better than that of an RBF kernel optimized by grid search.作者: Somber 時間: 2025-3-29 11:33 作者: 營養(yǎng) 時間: 2025-3-29 19:17 作者: 谷類 時間: 2025-3-29 20:49 作者: 臨時抱佛腳 時間: 2025-3-30 03:22
https://doi.org/10.1007/978-3-663-02438-5e full supervised training by gradient descent proposed recently in same papers. We conclude that a fully supervised training performs generally better. We also compare . with . and we conclude that . suppose a reduction in the number of iterations.作者: predict 時間: 2025-3-30 07:18
The Globalizing of the University,, feed-forward neural networks were used to estimate the ammonium concentration in the effluent stream of the biological plant. The architecture of the neural network is based on previous works in this topic. The methodology consists in performing a group of different sizes of the hidden layer and different subsets of input variables.作者: 享樂主義者 時間: 2025-3-30 09:51
Festschrift in Honor of R. Dennis Cook with each face orientation. To select the best localization hypothesis, we combine radiometric and probabilistic information. The method is quite fast and accurate. The mean localization error (estimated on more than 700 test images) is lower than 9%.作者: BYRE 時間: 2025-3-30 12:48 作者: Evolve 時間: 2025-3-30 19:49
An Experimental Study on Training Radial Basis Functions by Gradient Descente full supervised training by gradient descent proposed recently in same papers. We conclude that a fully supervised training performs generally better. We also compare . with . and we conclude that . suppose a reduction in the number of iterations.作者: chance 時間: 2025-3-30 21:30 作者: 天賦 時間: 2025-3-31 01:32 作者: 幻影 時間: 2025-3-31 08:23
,Wintergrüne G?rten in Mitteleuropa,M is experimented in the field of speech recognition in order to evaluate its performance for such time variable application and to prove that biological models are capable of giving good results as stochastic and hybrid ones.作者: 軍火 時間: 2025-3-31 12:18 作者: ANT 時間: 2025-3-31 15:29 作者: deforestation 時間: 2025-3-31 18:32 作者: Middle-Ear 時間: 2025-3-31 23:11 作者: Esalate 時間: 2025-4-1 03:52
Comparison Between Two Spatio-Temporal Organization Maps for Speech RecognitionM is experimented in the field of speech recognition in order to evaluate its performance for such time variable application and to prove that biological models are capable of giving good results as stochastic and hybrid ones.作者: 無彈性 時間: 2025-4-1 06:09
A Study of the Robustness of KNN Classifiers Trained Using Soft Labels reveal that learning with soft labels is more robust against label errors opposed to learning with crisp labels. The proposed technique to find soft labels from the data, was also found to lead to a more robust training in most data sets investigated.作者: Acumen 時間: 2025-4-1 13:10
Incremental Manifold Learning Via Tangent Space Alignmentsed, where landmarks are selected based on LASSO regression, which is well known to favor sparse approximations because it uses regularization with l. norm. Furthermore, an incremental version (ILLTSA) of LLTSA is also proposed. Experimental results on synthetic data and real word data sets demonstrate the effectivity of our algorithms.作者: arrogant 時間: 2025-4-1 16:26 作者: NUDGE 時間: 2025-4-1 19:42 作者: Pde5-Inhibitors 時間: 2025-4-2 00:50
