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71.
In this paper, we propose a scheme to integrate independent component analysis (ICA) and neural networks for electrocardiogram (ECG) beat classification. The ICA is used to decompose ECG signals into weighted sum of basic components that are statistically mutual independent. The projections on these components, together with the RR interval, then constitute a feature vector for the following classifier. Two neural networks, including a probabilistic neural network (PNN) and a back-propagation neural network (BPNN), are employed as classifiers. ECG samples attributing to eight different beat types were sampled from the MIT-BIH arrhythmia database for experiments. The results show high classification accuracy of over 98% with either of the two classifiers. Between them, the PNN shows a slightly better performance than BPNN in terms of accuracy and robustness to the number of ICA-bases. The impressive results prove that the integration of independent component analysis and neural networks, especially PNN, is a promising scheme for the computer-aided diagnosis of heart diseases based on ECG. 相似文献
72.
Wei-Chou Chen Shian-Shyong Tseng Tzung-Pei Hong 《Expert systems with applications》2008,34(4):2858-2869
Feature selection is about finding useful (relevant) features to describe an application domain. Selecting relevant and enough features to effectively represent and index the given dataset is an important task to solve the classification and clustering problems intelligently. This task is, however, quite difficult to carry out since it usually needs a very time-consuming search to get the features desired. This paper proposes a bit-based feature selection method to find the smallest feature set to represent the indexes of a given dataset. The proposed approach originates from the bitmap indexing and rough set techniques. It consists of two-phases. In the first phase, the given dataset is transformed into a bitmap indexing matrix with some additional data information. In the second phase, a set of relevant and enough features are selected and used to represent the classification indexes of the given dataset. After the relevant and enough features are selected, they can be judged by the domain expertise and the final feature set of the given dataset is thus proposed. Finally, the experimental results on different data sets also show the efficiency and accuracy of the proposed approach. 相似文献
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75.
基于排序的关联分类算法 总被引:1,自引:0,他引:1
提出了一种基于排序的关联分类算法.利用基于规则的分类方法中择优方法偏爱高精度规则的思想和考虑尽可能多的规则,改进了CBA(Classification Based on Associations)只根据少数几条覆盖训练集的规则构造分类器的片面性.首先采用关联规则挖掘算法产生后件为类标号的关联规则,然后根据长度、置信度、支持度和提升度等对规则进行排序,并在排序时删除对分类结果没有影响的规则.排序后的规则加上一个默认分类便构成最终的分类器.选用20个UCI公共数据集的实验结果表明,提出的算法比CBA具有更高的平均分类精度. 相似文献
76.
利用LS—SVM模块化决策系统求解EEG源参数 总被引:1,自引:1,他引:0
给定头皮脑电位的分布推算脑内电活动的源是脑电研究的一个重要的方面.研究涉及到信息科学、电磁场计算及生物医学工程等多个学科领域,其研究成果在神经疾病诊断、探索人的感觉和认知过程等方面具有蕈要意义.基于最小二乘支持向最机(LS-SVM)算法建立模块化决策系统,首先对脑电数据进行分类,然后依据分类结果提取数据样本,并建立回归模型,最后求解多种偶极子源参数.从而建立起头皮电压和脑电源参数之间的内在联系,为脑电动态分析提出一种实时的研究思路.计算机仿真计算结果证明了此方法的有效性. 相似文献
77.
针对Internet日益增多的攻击现状,防火墙、入侵检侧系统等网络安全技术发展日益成熟。但是现实中总有一些攻击能够成功,我们就有必要研究在遭受攻击情况下分析网络的脆弱性技术及及时的恢复技术,最小化对于网络不利影响。本文提出了计算机网络脆弱性的概念。分析脆弱性存在的深层原因,对网络脆弱性提供了初步的了解。 相似文献
78.
Anytime algorithms have been proposed for many different applications, e.g., in data mining. Their strengths are the ability
to first provide a result after a very short initialization and second to improve their result with additional time. Therefore,
anytime algorithms have so far been used when the available processing time varies, e.g., on varying data streams. In this
paper we propose to employ anytime algorithms on constant data streams, i.e., for tasks with constant time allowance. We introduce
two approaches that harness the strengths of anytime algorithms on constant data streams and thereby improve the over all
quality of the result with respect to the corresponding budget algorithm. We derive formulas for the expected performance
gain and demonstrate the effectiveness of our novel approaches using existing anytime algorithms on benchmark data sets. 相似文献
79.
Flexible decision tree for data stream classification in the presence of concept change, noise and missing values 总被引:1,自引:0,他引:1
In recent years, classification learning for data streams has become an important and active research topic. A major challenge
posed by data streams is that their underlying concepts can change over time, which requires current classifiers to be revised
accordingly and timely. To detect concept change, a common methodology is to observe the online classification accuracy. If
accuracy drops below some threshold value, a concept change is deemed to have taken place. An implicit assumption behind this
methodology is that any drop in classification accuracy can be interpreted as a symptom of concept change. Unfortunately however,
this assumption is often violated in the real world where data streams carry noise that can also introduce a significant reduction
in classification accuracy. To compound this problem, traditional noise cleansing methods are incompetent for data streams.
Those methods normally need to scan data multiple times whereas learning for data streams can only afford one-pass scan because
of data’s high speed and huge volume. Another open problem in data stream classification is how to deal with missing values.
When new instances containing missing values arrive, how a learning model classifies them and how the learning model updates
itself according to them is an issue whose solution is far from being explored. To solve these problems, this paper proposes
a novel classification algorithm, flexible decision tree (FlexDT), which extends fuzzy logic to data stream classification.
The advantages are three-fold. First, FlexDT offers a flexible structure to effectively and efficiently handle concept change. Second, FlexDT is robust to noise. Hence it can prevent noise
from interfering with classification accuracy, and accuracy drop can be safely attributed to concept change. Third, it deals
with missing values in an elegant way. Extensive evaluations are conducted to compare FlexDT with representative existing
data stream classification algorithms using a large suite of data streams and various statistical tests. Experimental results
suggest that FlexDT offers a significant benefit to data stream classification in real-world scenarios where concept change,
noise and missing values coexist. 相似文献
80.
本文对目前比较优秀的各种分类方法进行了介绍、分析和比较.在此基础上,借鉴决策树方法的快速分类特性,提出了一种基于数据库抽样的海量数据分类算法,给出了算法的设计思想和实现原理,并对多处理环境下的优化进行了探讨.实验研究表明,该算法可以明显提高海量数据库的分类效率. 相似文献