標(biāo)題: Titlebook: Advances in Intelligent Signal Processing and Data Mining; Theory and Applicati Petia Georgieva,Lyudmila Mihaylova,Lakhmi C Jain Book 2013 [打印本頁] 作者: 大腦 時(shí)間: 2025-3-21 18:04
書目名稱Advances in Intelligent Signal Processing and Data Mining影響因子(影響力)
書目名稱Advances in Intelligent Signal Processing and Data Mining影響因子(影響力)學(xué)科排名
書目名稱Advances in Intelligent Signal Processing and Data Mining網(wǎng)絡(luò)公開度
書目名稱Advances in Intelligent Signal Processing and Data Mining網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Advances in Intelligent Signal Processing and Data Mining被引頻次
書目名稱Advances in Intelligent Signal Processing and Data Mining被引頻次學(xué)科排名
書目名稱Advances in Intelligent Signal Processing and Data Mining年度引用
書目名稱Advances in Intelligent Signal Processing and Data Mining年度引用學(xué)科排名
書目名稱Advances in Intelligent Signal Processing and Data Mining讀者反饋
書目名稱Advances in Intelligent Signal Processing and Data Mining讀者反饋學(xué)科排名
作者: chalice 時(shí)間: 2025-3-21 21:31 作者: Hemodialysis 時(shí)間: 2025-3-22 02:45 作者: 易受刺激 時(shí)間: 2025-3-22 07:41 作者: TSH582 時(shí)間: 2025-3-22 09:31
Computational Intelligence in Automotive Applications,ane detection algorithms can be separated into lane modeling, feature extraction and model parameter estimation. Each of these steps is discussed in detail with examples and results. A recently proposed lane feature extraction approach, which is called the Global Lane Feature Refinement Algorithm (G作者: vitreous-humor 時(shí)間: 2025-3-22 14:34
Detecting Anomalies in Sensor Signals Using Database Technology,cessed by tracking systems which allows for enhancing the provided kinematic information. In high-level fusion systems, this kinematic information can be combined with additional domain-specific data which allows for detecting object behavior and threat patterns. These systems contribute to situatio作者: Obedient 時(shí)間: 2025-3-22 18:12
Hierarchical Clustering for Large Data Sets,ated and the scaling of hierarchical clustering in time and memory is discussed. A new method for speeding up hierarchical clustering with cluster seeding is introduced, and this method is compared with a traditional agglomerative hierarchical, average link clustering algorithm using several interna作者: DEVIL 時(shí)間: 2025-3-22 21:15
A Novel Framework for Object Recognition under Severe Occlusion,he comparison of assemblies of image regions with a previously stored view of a known prototype. Shape context representation and matching are employed for recovering point correspondences between the image and the prototype. Assuming that the prototype view is sufficiently similar in configuration 作者: jagged 時(shí)間: 2025-3-23 04:26
Historical Consistent Neural Networks: New Perspectives on Market Modeling, Forecasting and Risk Anigh degree of nonlinearity. In this chapter we deal with a special type of time-delay recurrent neural networks. In these models we understand a part of the world as a large recursive system which is only partially observable. We model and forecast all observables, avoiding the problem in open syste作者: NAVEN 時(shí)間: 2025-3-23 08:03 作者: 全部逛商店 時(shí)間: 2025-3-23 12:28 作者: 溫順 時(shí)間: 2025-3-23 16:52 作者: 知識(shí)分子 時(shí)間: 2025-3-23 19:06 作者: 音樂學(xué)者 時(shí)間: 2025-3-24 02:02
Constraining Shape and Size in Clusteringding is introduced, and this method is compared with a traditional agglomerative hierarchical, average link clustering algorithm using several internal and external cluster validation indices. A benchmark study compares the cluster performance of both approaches using a wide variety of real-world and artificial benchmark data sets.作者: LATER 時(shí)間: 2025-3-24 05:20 作者: 領(lǐng)導(dǎo)權(quán) 時(shí)間: 2025-3-24 07:23 作者: 公司 時(shí)間: 2025-3-24 10:52 作者: Facet-Joints 時(shí)間: 2025-3-24 15:08
Monte Carlo-Based Bayesian Group Object Tracking and Causal Reasoning,t agents based exclusively on their observed trajectories.We use these as building blocks for developing a unified tracking and behavioral reasoning paradigm. Both synthetic and realistic examples are provided for demonstrating the derived concepts.作者: diskitis 時(shí)間: 2025-3-24 22:48 作者: 聯(lián)想 時(shí)間: 2025-3-25 01:10 作者: conifer 時(shí)間: 2025-3-25 05:12 作者: 使迷惑 時(shí)間: 2025-3-25 10:18 作者: Dysarthria 時(shí)間: 2025-3-25 13:35
A Sequential Monte Carlo Method for Multi-target Tracking with the Intensity Filter,y in simulations but also in real world applications. In addition it can be shown that the performance of the PHD filter decreases substantially if the a priori knowledge of the clutter intensity is chosen incorrectly.作者: 預(yù)兆好 時(shí)間: 2025-3-25 17:55 作者: Blanch 時(shí)間: 2025-3-25 20:26
Reinforcement Learning with Neural Networks: Tricks of the Trade,y for one to understand how to apply reinforcement learning using a neural network. Additionally, we describe two example implementations of reinforcement learning using the board games of Tic-Tac-Toe and Chung Toi, a challenging extension to Tic-Tac-Toe.作者: deactivate 時(shí)間: 2025-3-26 00:36 作者: 殘暴 時(shí)間: 2025-3-26 04:35 作者: Free-Radical 時(shí)間: 2025-3-26 08:39
https://doi.org/10.1007/978-3-319-45805-2t agents based exclusively on their observed trajectories.We use these as building blocks for developing a unified tracking and behavioral reasoning paradigm. Both synthetic and realistic examples are provided for demonstrating the derived concepts.作者: fiscal 時(shí)間: 2025-3-26 14:44
Advances in Intelligent Signal Processing and Data MiningTheory and Applicati作者: 易達(dá)到 時(shí)間: 2025-3-26 18:33 作者: 高深莫測 時(shí)間: 2025-3-26 21:14 作者: 感染 時(shí)間: 2025-3-27 04:34
Event Detection in Environmental Scanning filter, the Gaussian particle filter and the Gaussian sum particles filter) quantitatively and provide insightful result analysis and suggestions. Furthermore, the influence of featuremaps on the tracking performance is also investigated.作者: 富足女人 時(shí)間: 2025-3-27 08:29 作者: Condescending 時(shí)間: 2025-3-27 10:21 作者: follicular-unit 時(shí)間: 2025-3-27 16:03 作者: Leaven 時(shí)間: 2025-3-27 18:32 作者: 表示向前 時(shí)間: 2025-3-27 23:46
Detecting Anomalies in Sensor Signals Using Database Technology,erns for the detection of anomalies in tracking scenarios can be expressed in relational algebra. Finally, we present an application of such a system for monitoring and analyzing air traffic using a commercial database management system.作者: Paradox 時(shí)間: 2025-3-28 03:09 作者: 無動(dòng)于衷 時(shí)間: 2025-3-28 08:00 作者: 混沌 時(shí)間: 2025-3-28 13:36 作者: Eclampsia 時(shí)間: 2025-3-28 14:52
https://doi.org/10.1007/978-3-658-24355-5he-art of localization techniques. Next, it formulates the problem of localization within Bayesian framework and presents sequential Monte Carlo methods for localization based on received signal strength indicators (RSSIs). Multiple model particle filters are developed and their performance is evalu作者: Expurgate 時(shí)間: 2025-3-28 21:25 作者: Heterodoxy 時(shí)間: 2025-3-29 01:02 作者: 在駕駛 時(shí)間: 2025-3-29 04:05 作者: stress-response 時(shí)間: 2025-3-29 09:20
Constraining Shape and Size in Clusteringated and the scaling of hierarchical clustering in time and memory is discussed. A new method for speeding up hierarchical clustering with cluster seeding is introduced, and this method is compared with a traditional agglomerative hierarchical, average link clustering algorithm using several interna作者: Ejaculate 時(shí)間: 2025-3-29 11:55
Event Detection in Environmental Scanninghe comparison of assemblies of image regions with a previously stored view of a known prototype. Shape context representation and matching are employed for recovering point correspondences between the image and the prototype. Assuming that the prototype view is sufficiently similar in configuration 作者: Vsd168 時(shí)間: 2025-3-29 18:53
A. Sch?nhuth,I. G. Costa,A. Schliepigh degree of nonlinearity. In this chapter we deal with a special type of time-delay recurrent neural networks. In these models we understand a part of the world as a large recursive system which is only partially observable. We model and forecast all observables, avoiding the problem in open syste作者: 存在主義 時(shí)間: 2025-3-29 21:03
Peter M. Kappeler,Carel P. Schaikbased on interactions between an agent and its environment. Through repeated interactions with the environment, and the receipt of rewards, the agent learns which actions are associated with the greatest cumulative reward..This work describes the computational implementation of reinforcement learnin
