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1.
支持向量机的算法研究   总被引:1,自引:0,他引:1  
支持向量机(support vector machine,SVM)是20世纪90年代发展起来的一种新型机器学习方法,是在统计学习理论基础上发展起来的一种新的数据挖掘方法,已广泛应用于模式识别与回归分析.并已成为国际机器学习界的研究热点.本文主要讨论其基本原理与SVM训练算法.  相似文献   
2.
基于支持向量机的软测量建模方法的应用   总被引:1,自引:0,他引:1  
利用基于最小二乘支持向量机(LS-SVM)的软测量建模方法,通过工业现场数据来对丁二烯精馏装置建立软测量模型.对于该软测量模型,支持向量机方法比BP神经网络方法具有更好的泛化能力.研究结果表明,基于最小二乘的支持向量机建模方法是一种有效的软测量建模方法.  相似文献   
3.
使用增量SVM进行文本分类   总被引:1,自引:1,他引:0  
针对传统SVM无法适应文本数据库随着时间不断更新的问题,通过对新增文本集的KKT条件的分析,研究了加入新增文本集后支持向量集的变化,提出了使用增量SVM进行文本分类的算法,并通过实验验证了通过该算法得到的分类器和传统分类器有着相似的分类能力和泛化能力.  相似文献   
4.
一种SVM增量学习算法   总被引:17,自引:6,他引:11  
分析了SVM理论中SV(支持向量)集的特点,给出一种SVM增量学习算法,通过在增量学习中使用SV集与训练样本集的分类等价性,使得新的增量训练无需在整个训练样本空间进行,理论分析和实验结果表明,该算法能然保证分类精度的同时有效地提高训练速度。  相似文献   
5.
Seven factors, including the maximum volume of once flow , occurrence frequency of debris flow , watershed area , main channel length , watershed relative height difference , valley incision density and the length ratio of sediment supplement are chosen as evaluation factors of debris flow hazard degree. Using support vector machine (SVM) theory, we selected 259 basic data of 37 debris flow channels in Yunnan Province as learning samples in this study. We create a debris flow hazard assessment model based on SVM. The model was validated though instance applications and showed encouraging results.  相似文献   
6.
Content-based filtering E-commerce recommender system was discussed fully in this paper. Users' unique features can be explored by means of vector space model firstly. Then based on the qualitative value of products informa tion, the recommender lists were obtained. Since the system can adapt to the users' feedback automatically, its performance were enhanced comprehensively. Finally the evaluation of the system and the experimental results were presented.  相似文献   
7.
This article presents an anomaly detection system based on principal component analysis (PCA) and support vector machine (SVM). The system first creates a profile defining a normal behavior by frequency-based scheme, and then compares the similarity of a current behavior with the created profile to decide whether the input instance is norreal or anomaly. In order to avoid overfitting and reduce the computational burden, normal behavior principal features are extracted by the PCA method. SVM is used to distinguish normal or anomaly for user behavior after training procedure has been completed by learning. In the experiments for performance evaluation the system achieved a correct detection rate equal to 92.2% and a false detection rate equal to 2.8%.  相似文献   
8.
Intrusion detection using rough set classification   总被引:6,自引:0,他引:6  
Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of "IF-THEN" rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).  相似文献   
9.
This novel method of Pedestrian Tracking using Support Vector (PTSV) proposed for a video surveillance instrument combines the Support Vector Machine (SVM) classifier into an optic-flow based tracker. The traditional method using optical flow tracks objects by minimizing an intensity difference function between successive frames, while PTSV tracks objects by maximizing the SVM classification score. As the SVM classifier for object and non-object is pre-trained, there is need only to classify an image block as object or non-ob-ject without having to compare the pixel region of the tracked object in the previous frame. To account for large motions between successive frames we build pyramids from the support vectors and use a coarse-to-fine scan in the classification stage. To accelerate the training of SVM, a Sequential Minimal Optimization Method (SMO) is adopted. The results of using a kernel-PTSV for pedestrian tracking from real time video are shown at the end. Comparative experimental results showed that PTSV improves the reliability of tracking compared to that of traditional tracking method using optical flow.  相似文献   
10.
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