全文获取类型
收费全文 | 32326篇 |
免费 | 5963篇 |
国内免费 | 4335篇 |
学科分类
工业技术 | 42624篇 |
出版年
2024年 | 174篇 |
2023年 | 754篇 |
2022年 | 1482篇 |
2021年 | 1761篇 |
2020年 | 1653篇 |
2019年 | 1231篇 |
2018年 | 1111篇 |
2017年 | 1282篇 |
2016年 | 1369篇 |
2015年 | 1509篇 |
2014年 | 2175篇 |
2013年 | 1950篇 |
2012年 | 2436篇 |
2011年 | 2653篇 |
2010年 | 2115篇 |
2009年 | 2149篇 |
2008年 | 2188篇 |
2007年 | 2439篇 |
2006年 | 2062篇 |
2005年 | 1718篇 |
2004年 | 1432篇 |
2003年 | 1273篇 |
2002年 | 1086篇 |
2001年 | 734篇 |
2000年 | 634篇 |
1999年 | 581篇 |
1998年 | 451篇 |
1997年 | 338篇 |
1996年 | 284篇 |
1995年 | 252篇 |
1994年 | 207篇 |
1993年 | 181篇 |
1992年 | 135篇 |
1991年 | 76篇 |
1990年 | 75篇 |
1989年 | 85篇 |
1988年 | 56篇 |
1987年 | 38篇 |
1986年 | 39篇 |
1985年 | 48篇 |
1984年 | 45篇 |
1983年 | 60篇 |
1982年 | 44篇 |
1981年 | 42篇 |
1980年 | 33篇 |
1979年 | 28篇 |
1978年 | 29篇 |
1977年 | 29篇 |
1976年 | 24篇 |
1974年 | 12篇 |
排序方式: 共有10000条查询结果,搜索用时 125 毫秒
1.
现代战场中的无线通信设备日益增多,精准获取个体信息已成为研究热点,但也是难点。针对通信电台,提出了一种分选识别技术。该技术从电台物理层特性出发,对其辐射信号的细微特征进行K-means聚类以实现分选,分选的同时提取各个个体的特征属性值,未知信号通过与特征属性值相关运算实现个体识别。该技术无需先验知识,无需训练运算,通过实验验证,其可行、高效,易于工程实现。 相似文献
2.
The International Society for the Study of Vascular Anomalies (ISSVA) provides a classification for vascular anomalies that enables specialists to unambiguously classify diagnoses. This classification is only available in PDF format and is not machine-readable, nor does it provide unique identifiers that allow for structured registration. In this paper, we describe the process of transforming the ISSVA classification into an ontology. We also describe the structure of this ontology, as well as two applications of the ontology using examples from the domain of rare disease research. We used the expertise of an ontology expert and clinician during the development process. We semi-automatically added mappings to relevant external ontologies using automated ontology matching systems and manual assessment by experts. The ISSVA ontology should contribute to making data for vascular anomaly research more Findable, Accessible, Interoperable, and Reusable (FAIR). The ontology is available at https://bioportal.bioontology.org/ontologies/ISSVA. 相似文献
3.
Alexander Detzner Martin Eigner 《Quality and Reliability Engineering International》2021,37(1):335-351
Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data. 相似文献
4.
5.
Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning. 相似文献
6.
7.
8.
Although greedy algorithms possess high efficiency, they often receive suboptimal solutions of the ensemble pruning problem, since their exploration areas are limited in large extent. And another marked defect of almost all the currently existing ensemble pruning algorithms, including greedy ones, consists in: they simply abandon all of the classifiers which fail in the competition of ensemble selection, causing a considerable waste of useful resources and information. Inspired by these observations, an interesting greedy Reverse Reduce-Error (RRE) pruning algorithm incorporated with the operation of subtraction is proposed in this work. The RRE algorithm makes the best of the defeated candidate networks in a way that, the Worst Single Model (WSM) is chosen, and then, its votes are subtracted from the votes made by those selected components within the pruned ensemble. The reason is because, for most cases, the WSM might make mistakes in its estimation for the test samples. And, different from the classical RE, the near-optimal solution is produced based on the pruned error of all the available sequential subensembles. Besides, the backfitting step of RE algorithm is replaced with the selection step of a WSM in RRE. Moreover, the problem of ties might be solved more naturally with RRE. Finally, soft voting approach is employed in the testing to RRE algorithm. The performances of RE and RRE algorithms, and two baseline methods, i.e., the method which selects the Best Single Model (BSM) in the initial ensemble, and the method which retains all member networks of the initial ensemble (ALL), are evaluated on seven benchmark classification tasks under different initial ensemble setups. The results of the empirical investigation show the superiority of RRE over the other three ensemble pruning algorithms. 相似文献
9.
This paper presents an innovative solution to model distributed adaptive systems in biomedical environments. We present an original TCBR-HMM (Text Case Based Reasoning-Hidden Markov Model) for biomedical text classification based on document content. The main goal is to propose a more effective classifier than current methods in this environment where the model needs to be adapted to new documents in an iterative learning frame. To demonstrate its achievement, we include a set of experiments, which have been performed on OSHUMED corpus. Our classifier is compared with Naive Bayes and SVM techniques, commonly used in text classification tasks. The results suggest that the TCBR-HMM Model is indeed more suitable for document classification. The model is empirically and statistically comparable to the SVM classifier and outperforms it in terms of time efficiency. 相似文献
10.