標(biāo)題: Titlebook: Neural Information Processing; 24th International C Derong Liu,Shengli Xie,El-Sayed M. El-Alfy Conference proceedings 2017 Springer Interna [打印本頁] 作者: 美麗動人 時間: 2025-3-21 16:38
書目名稱Neural Information Processing影響因子(影響力)
書目名稱Neural Information Processing影響因子(影響力)學(xué)科排名
書目名稱Neural Information Processing網(wǎng)絡(luò)公開度
書目名稱Neural Information Processing網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Neural Information Processing被引頻次
書目名稱Neural Information Processing被引頻次學(xué)科排名
書目名稱Neural Information Processing年度引用
書目名稱Neural Information Processing年度引用學(xué)科排名
書目名稱Neural Information Processing讀者反饋
書目名稱Neural Information Processing讀者反饋學(xué)科排名
作者: PAGAN 時間: 2025-3-21 23:25 作者: 搜集 時間: 2025-3-22 03:56 作者: 磨坊 時間: 2025-3-22 04:58 作者: cyanosis 時間: 2025-3-22 12:48
0302-9743 e proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 ?full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on?Machine Learning,作者: Allowance 時間: 2025-3-22 16:45
Fuzzy Self-Organizing Incremental Neural Network for Fuzzy Clusteringd due to the self-adjusting nodes and edges which fit the learning data incrementally. A removal of nodes and edges promises the robustness of the network to the noisy data. Experiments on artificial and real-world data prove the validity of the clustering method.作者: 注射器 時間: 2025-3-22 18:36
Topology Learning Embedding: A Fast and Incremental Method for Manifold Learninger way: it constructs a topology preserving network rapidly and incrementally through online input data; then with the Isomap-based embedding strategy, it achieves out-of-sample data embedding efficiently. Experiments on synthetic data and real-world handwritten digit data demonstrate that TLE is a promising method for dimensionality reduction.作者: inventory 時間: 2025-3-22 22:51
Using Flexible Neural Trees to Seed BackpropagationWe show that putting the two methods together can yield very good results. The FNT solution can be embedded into a larger neural network that is then optimized using backpropagation. The combination of the two methods outperforms either method alone.作者: FIR 時間: 2025-3-23 04:28
Improving Generalization Capability of Extreme Learning Machine with Synthetic Instances Generation based on 4 representative regression datasets of KEEL demonstrate that our proposed SIGELM obviously improves the generalization capability of ELM and effectively decreases the phenomenon of over-fitting.作者: ACME 時間: 2025-3-23 05:41 作者: 教育學(xué) 時間: 2025-3-23 10:54
Hybrid RVM Algorithm Based on the Prediction Variancee the accuracy. Test results show that RVM with the biased wavelet kernel is able to get increased prediction precision considering data features and the predicted variance is an efficient metric to construct the hybrid algorithm.作者: Bronchial-Tubes 時間: 2025-3-23 13:51
A Self-adaptive Growing Method for Training Compact RBF Networkse number of nodes reaches a limit. Then the network is further optimized with a supervised fine-tuning method. Experimental results indicate that the proposed method could achieve better performances than traditional algorithms when training same sized RBF networks.作者: indemnify 時間: 2025-3-23 18:38 作者: 防銹 時間: 2025-3-23 22:11 作者: CLOWN 時間: 2025-3-24 03:30
Relation Classification via CNN, Segmented Max-pooling, and SDP-BLSTMtwo-layer feed-forward network for classification. Experiments on the SemEval-2010 Task 8 dataset show that our model achieves competitive performance when compared with several start-of-the-art models.作者: 沉著 時間: 2025-3-24 08:38 作者: Myocyte 時間: 2025-3-24 13:47
Conference proceedings 2017 Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.?.作者: 接合 時間: 2025-3-24 15:30 作者: 纖細(xì) 時間: 2025-3-24 21:09 作者: wall-stress 時間: 2025-3-25 01:28
Binary Stochastic Representations for Large Multi-class Classifications, but also learning to map inputs to binary codes. This approach called . keeps the sublinear inference complexity but do not need any . tuning. Experimental results on different datasets show the effectiveness of the approach w.r.t. baseline methods.作者: adequate-intake 時間: 2025-3-25 04:33 作者: 起波瀾 時間: 2025-3-25 09:53 作者: grieve 時間: 2025-3-25 14:05
Wei Ao,Yulin He,Joshua Zhexue Huang,Yupeng He and operational support are used to improve performance and compliance. Process mining starts from recorded events that are characterized by a case identifier, an activity name, a timestamp, and optional attributes like resource or costs. In many applications, there are multiple candidate identifie作者: Common-Migraine 時間: 2025-3-25 16:59
Yunxiao Shi,Xiangnan He,Han Wu,Zhong-Xiao Jin,Wenlian Luf a system. Software product lines pose similar challenges when the soundness between different branches of a product is at stake. Such challenges are usually tackled by engineering methods that focus on the development process, and not on the subject of attention, the code. The risk of code inconsi作者: 綠州 時間: 2025-3-25 23:51
Tianyue Zhang,Baile Xu,Furao Shenf a system. Software product lines pose similar challenges when the soundness between different branches of a product is at stake. Such challenges are usually tackled by engineering methods that focus on the development process, and not on the subject of attention, the code. The risk of code inconsi作者: CARK 時間: 2025-3-26 03:30 作者: 厭食癥 時間: 2025-3-26 06:28
Tao Zhu,Furao Shen,Jinxi Zhao,Yu Liangf a system. Software product lines pose similar challenges when the soundness between different branches of a product is at stake. Such challenges are usually tackled by engineering methods that focus on the development process, and not on the subject of attention, the code. The risk of code inconsi作者: 發(fā)電機(jī) 時間: 2025-3-26 09:54 作者: seroma 時間: 2025-3-26 16:34 作者: enfeeble 時間: 2025-3-26 16:52
Baile Xu,Furao Shen,Jinxi Zhao,Tianyue Zhang and operational support are used to improve performance and compliance. Process mining starts from recorded events that are characterized by a case identifier, an activity name, a timestamp, and optional attributes like resource or costs. In many applications, there are multiple candidate identifie作者: 孤獨無助 時間: 2025-3-26 21:44 作者: 動作謎 時間: 2025-3-27 01:28 作者: PRO 時間: 2025-3-27 08:25
Yosuke Fukuchi,Masahiko Osawa,Hiroshi Yamakawa,Michita Imai For example, users of a system may want new functionality or performance enhancements to cope with growing user population (changing requirements). Alternatively, vendors of a system may want to minimize costs in implementing requirements changes (evolution requirements). We propose to use Constrai作者: inveigh 時間: 2025-3-27 10:00
Peng Wu,Jeff Orchardpeople who move in a certain area systematically. Indeed, due to their expertise, they very often prefer different solutions. In this paper we provide an analytic model to study the deviations of the systematic movements from the paths proposed by a route planner. As proxy of human mobility we use r作者: aggravate 時間: 2025-3-27 15:19
Dinh Tran-Van,Alessandro Sperduti,Fabrizio Costapeople who move in a certain area systematically. Indeed, due to their expertise, they very often prefer different solutions. In this paper we provide an analytic model to study the deviations of the systematic movements from the paths proposed by a route planner. As proxy of human mobility we use r作者: FUME 時間: 2025-3-27 20:08
Cheng Xu,Yue Wu,Zongtian Liupeople who move in a certain area systematically. Indeed, due to their expertise, they very often prefer different solutions. In this paper we provide an analytic model to study the deviations of the systematic movements from the paths proposed by a route planner. As proxy of human mobility we use r作者: 單獨 時間: 2025-3-27 22:18 作者: 暗語 時間: 2025-3-28 05:56 作者: 知識分子 時間: 2025-3-28 06:27
Thomas Gerald,Nicolas Baskiotis,Ludovic Denoyerecking. Unfortunately, these notations are complex and often difficult to understand from a human point of view especially for engineers who are not familiar with formal methods. Several research works have proposed tools to support formal models using graphical views. On the one hand, such views ar作者: KEGEL 時間: 2025-3-28 12:30 作者: ventilate 時間: 2025-3-28 18:26
Improving Generalization Capability of Extreme Learning Machine with Synthetic Instances Generation of ELM with Synthetic Instances Generation (SIGELM). We focus on optimizing the output-layer weights via adding informative synthetic instances to the training dataset at each learning step. In order to get the required synthetic instances, a neighborhood is determined for each high-uncertainty tra作者: buoyant 時間: 2025-3-28 21:55
Adaptive , , Regularization: Oracle Property and Applications sample size. Other than the case of the number of covariates is smaller than the sample size, in this paper, we prove that under appropriate conditions, these adaptive . estimators possess the oracle property in the case that the number of covariates is much larger than the sample size. We present 作者: negotiable 時間: 2025-3-29 01:03 作者: 難解 時間: 2025-3-29 03:37 作者: 業(yè)余愛好者 時間: 2025-3-29 11:07 作者: 圣人 時間: 2025-3-29 12:43
Hybrid RVM Algorithm Based on the Prediction Variance solve nonlinear problems by using kernel functions. Biased wavelets are localized in time and infrequency but, unlike wavelets, have adjustable nonzero mean. The proposed hybrid algorithm employs a family of biased wavelets to construct the kernel functions of RVM, which makes the kernel of RVM mor作者: Nomadic 時間: 2025-3-29 16:55 作者: 訓(xùn)誡 時間: 2025-3-29 22:17
A Self-adaptive Growing Method for Training Compact RBF Networksrtional to the number of nodes in its hidden layer, while there is also a positive correlation between the number of nodes and the predication accuracy. In this paper, we propose a new training algorithm for RBF networks in order to construct high accuracy networks with as few nodes as possible. The作者: 玷污 時間: 2025-3-30 02:53
Incremental Extreme Learning Machine via Fast Random Search Methodo avoid this problem, enhanced random search based incremental extreme learning machine (EI-ELM) is proposed. However, we find that the EI-ELM’s training time is too long. In addition, EI-ELM can only add hidden nodes one by one. This paper proposes a fast method for EI-ELM (referred to as FI-ELM). 作者: Wallow 時間: 2025-3-30 07:52
Learning of Phase-Amplitude-Type Complex-Valued Neural Networks with Application to Signal Coherenceation functions, which can be applied to deal with coherent signals effectively. The performance of the proposed L-BFGS algorithm is compared with traditional complex-valued stochastic gradient descent method on the tasks of wave-related signal processing with various degrees of coherence. The exper作者: 先鋒派 時間: 2025-3-30 08:16 作者: 毀壞 時間: 2025-3-30 16:03
Using Flexible Neural Trees to Seed Backpropagationral trees (FNTs) are good at finding a globally near-optimal network to fit a dataset, using evolutionary algorithms and particle swarm optimization. We show that putting the two methods together can yield very good results. The FNT solution can be embedded into a larger neural network that is then 作者: 顛簸地移動 時間: 2025-3-30 19:41
Joint Neighborhood Subgraphs Link Predictions between proteins to explain the mechanism of a disease in biological networks, or to suggest novel products for a customer in a e-commerce recommendation system. Most link prediction approaches however do not effectively exploit the contextual information available in the neighborhood of each edge作者: 衰弱的心 時間: 2025-3-30 21:39
Multimodal Fusion with Global and Local Features for Text Classificationan ongoing challenge. In this paper, we propose an ensemble model which outperforms the state-of-the-art. We first utilize rule-based n-gram approach to extend corpus. Then two different features, global dependencies of word and local semantic feature, are extracted by gated recurrent unit and globa作者: 謙虛的人 時間: 2025-3-31 01:33 作者: 轉(zhuǎn)向 時間: 2025-3-31 06:18 作者: 小說 時間: 2025-3-31 11:33 作者: Ethics 時間: 2025-3-31 15:47 作者: 使殘廢 時間: 2025-3-31 17:51 作者: 缺乏 時間: 2025-3-31 22:52
https://doi.org/10.1007/978-3-319-70087-8Adaptive dynamic programming; Artificial intelligence; Biologically inspired computing; Brain-computer 作者: 寒冷 時間: 2025-4-1 02:03
Derong Liu,Shengli Xie,El-Sayed M. El-AlfyIncludes supplementary material: 作者: Ballerina 時間: 2025-4-1 08:10 作者: 忙碌 時間: 2025-4-1 11:42
