全文获取类型
收费全文 | 25020篇 |
免费 | 5137篇 |
国内免费 | 4348篇 |
专业分类
电工技术 | 1473篇 |
技术理论 | 2篇 |
综合类 | 2695篇 |
化学工业 | 888篇 |
金属工艺 | 338篇 |
机械仪表 | 919篇 |
建筑科学 | 573篇 |
矿业工程 | 139篇 |
能源动力 | 327篇 |
轻工业 | 327篇 |
水利工程 | 171篇 |
石油天然气 | 174篇 |
武器工业 | 96篇 |
无线电 | 2619篇 |
一般工业技术 | 1591篇 |
冶金工业 | 1946篇 |
原子能技术 | 36篇 |
自动化技术 | 20191篇 |
出版年
2024年 | 466篇 |
2023年 | 2011篇 |
2022年 | 3161篇 |
2021年 | 3096篇 |
2020年 | 2505篇 |
2019年 | 1691篇 |
2018年 | 1211篇 |
2017年 | 1061篇 |
2016年 | 1105篇 |
2015年 | 1091篇 |
2014年 | 1485篇 |
2013年 | 1294篇 |
2012年 | 1302篇 |
2011年 | 1569篇 |
2010年 | 1271篇 |
2009年 | 1271篇 |
2008年 | 1277篇 |
2007年 | 1153篇 |
2006年 | 982篇 |
2005年 | 900篇 |
2004年 | 699篇 |
2003年 | 593篇 |
2002年 | 538篇 |
2001年 | 403篇 |
2000年 | 350篇 |
1999年 | 288篇 |
1998年 | 267篇 |
1997年 | 224篇 |
1996年 | 185篇 |
1995年 | 149篇 |
1994年 | 118篇 |
1993年 | 115篇 |
1992年 | 96篇 |
1991年 | 46篇 |
1990年 | 47篇 |
1989年 | 48篇 |
1988年 | 29篇 |
1987年 | 28篇 |
1986年 | 41篇 |
1966年 | 14篇 |
1965年 | 24篇 |
1964年 | 25篇 |
1963年 | 23篇 |
1962年 | 12篇 |
1961年 | 17篇 |
1959年 | 15篇 |
1958年 | 16篇 |
1957年 | 22篇 |
1956年 | 13篇 |
1955年 | 23篇 |
排序方式: 共有10000条查询结果,搜索用时 15 毫秒
991.
User simulation in a stochastic dialog system 总被引:1,自引:1,他引:0
We present a new methodology of user simulation applied to the evaluation and refinement of stochastic dialog systems. Common weaknesses of these systems are the scarceness of the training corpus and the cost of an evaluation made by real users. We have considered the user simulation technique as an alternative way of testing and improving our dialog system. We have developed a new dialog manager that plays the role of the user. This user dialog manager incorporates several knowledge sources, combining statistical and heuristic information in order to define its dialog strategy. Once the user simulator is integrated into the dialog system, it is possible to enhance the dialog models by an automatic strategy learning. We have performed an extensive evaluation, achieving a slight but clear improvement of the dialog system. 相似文献
992.
Boosted Bayesian network classifiers 总被引:2,自引:0,他引:2
The use of Bayesian networks for classification problems has received a significant amount of recent attention. Although computationally
efficient, the standard maximum likelihood learning method tends to be suboptimal due to the mismatch between its optimization
criteria (data likelihood) and the actual goal of classification (label prediction accuracy). Recent approaches to optimizing
classification performance during parameter or structure learning show promise, but lack the favorable computational properties
of maximum likelihood learning. In this paper we present boosted Bayesian network classifiers, a framework to combine discriminative
data-weighting with generative training of intermediate models. We show that boosted Bayesian network classifiers encompass
the basic generative models in isolation, but improve their classification performance when the model structure is suboptimal.
We also demonstrate that structure learning is beneficial in the construction of boosted Bayesian network classifiers. On
a large suite of benchmark data-sets, this approach outperforms generative graphical models such as naive Bayes and TAN in
classification accuracy. Boosted Bayesian network classifiers have comparable or better performance in comparison to other
discriminatively trained graphical models including ELR and BNC. Furthermore, boosted Bayesian networks require significantly
less training time than the ELR and BNC algorithms. 相似文献
993.
Transfer in variable-reward hierarchical reinforcement learning 总被引:2,自引:1,他引:1
Transfer learning seeks to leverage previously learned tasks to achieve faster learning in a new task. In this paper, we consider
transfer learning in the context of related but distinct Reinforcement Learning (RL) problems. In particular, our RL problems are derived from Semi-Markov Decision Processes (SMDPs) that share the same
transition dynamics but have different reward functions that are linear in a set of reward features. We formally define the
transfer learning problem in the context of RL as learning an efficient algorithm to solve any SMDP drawn from a fixed distribution
after experiencing a finite number of them. Furthermore, we introduce an online algorithm to solve this problem, Variable-Reward
Reinforcement Learning (VRRL), that compactly stores the optimal value functions for several SMDPs, and uses them to optimally
initialize the value function for a new SMDP. We generalize our method to a hierarchical RL setting where the different SMDPs
share the same task hierarchy. Our experimental results in a simplified real-time strategy domain show that significant transfer
learning occurs in both flat and hierarchical settings. Transfer is especially effective in the hierarchical setting where
the overall value functions are decomposed into subtask value functions which are more widely amenable to transfer across
different SMDPs. 相似文献
994.
Inductive transfer with context-sensitive neural networks 总被引:1,自引:1,他引:0
Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of inductive transfer which uses a single output neural network and additional contextual inputs
for learning multiple tasks. Motivated by problems with the application of MTL networks to machine lifelong learning systems,
csMTL encoding of multiple task examples was developed and found to improve predictive performance. As evidence, the csMTL method is tested on seven task domains and shown to produce hypotheses for primary tasks that are often better than standard
MTL hypotheses when learning in the presence of related and unrelated tasks. We argue that the reason for this performance
improvement is a reduction in the number of effective free parameters in the csMTL network brought about by the shared output node and weight update constraints due to the context inputs. An examination
of IDT and SVM models developed from csMTL encoded data provides initial evidence that this improvement is not shared across all machine learning models. 相似文献
995.
Andras Ferencz Erik G. Learned-Miller Jitendra Malik 《International Journal of Computer Vision》2008,77(1-3):3-24
Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize
an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, inter-object
variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many
different instances of the category but few or just one positive “training” examples per object instance. Because variation
among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door
handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however,
standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from
a known category and builds an efficient “same” versus “different” classification cascade by predicting the most discriminative
features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature,
but also models the dependency between features, building an ordered sequence of discriminative features specific to the given
image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics
are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the
same algorithm for both cars and faces, outperforms a wide variety of other methods. 相似文献
996.
Juan Carlos Niebles Hongcheng Wang Li Fei-Fei 《International Journal of Computer Vision》2008,79(3):299-318
We present a novel unsupervised learning method for human action categories. A video sequence is represented as a collection
of spatial-temporal words by extracting space-time interest points. The algorithm automatically learns the probability distributions
of the spatial-temporal words and the intermediate topics corresponding to human action categories. This is achieved by using
latent topic models such as the probabilistic Latent Semantic Analysis (pLSA) model and Latent Dirichlet Allocation (LDA).
Our approach can handle noisy feature points arisen from dynamic background and moving cameras due to the application of the
probabilistic models. Given a novel video sequence, the algorithm can categorize and localize the human action(s) contained
in the video. We test our algorithm on three challenging datasets: the KTH human motion dataset, the Weizmann human action
dataset, and a recent dataset of figure skating actions. Our results reflect the promise of such a simple approach. In addition,
our algorithm can recognize and localize multiple actions in long and complex video sequences containing multiple motions. 相似文献
997.
We present an efficient method for learning part-based object class models from unsegmented images represented as sets of
salient features. A model includes parts’ appearance, as well as location and scale relations between parts. The object class
is generatively modeled using a simple Bayesian network with a central hidden node containing location and scale information,
and nodes describing object parts. The model’s parameters, however, are optimized to reduce a loss function of the training
error, as in discriminative methods. We show how boosting techniques can be extended to optimize the relational model proposed,
with complexity linear in the number of parts and the number of features per image. This efficiency allows our method to learn
relational models with many parts and features. The method has an advantage over purely generative and purely discriminative
approaches for learning from sets of salient features, since generative method often use a small number of parts and features,
while discriminative methods tend to ignore geometrical relations between parts. Experimental results are described, using
some bench-mark data sets and three sets of newly collected data, showing the relative merits of our method in recognition
and localization tasks. 相似文献
998.
Cheng-Jian Lin Yong-Cheng Liu Chi-Yung Lee 《Journal of Intelligent and Robotic Systems》2008,52(2):285-312
This study presents a wavelet-based neuro-fuzzy network (WNFN). The proposed WNFN model combines the traditional Takagi–Sugeno–Kang
(TSK) fuzzy model and the wavelet neural networks (WNN). This study adopts the non-orthogonal and compactly supported functions
as wavelet neural network bases. A novel supervised evolutionary learning, called WNFN-S, is proposed to tune the adjustable
parameters of the WNFN model. The proposed WNFN-S learning scheme is based on dynamic symbiotic evolution (DSE). The proposed
DSE uses the sequential-search-based dynamic evolutionary (SSDE) method. In some real-world applications, exact training data
may be expensive or even impossible to obtain. To solve this problem, the reinforcement evolutionary learning, called WNFN-R,
is proposed. Computer simulations have been conducted to illustrate the performance and applicability of the proposed WNFN-S
and WNFN-R learning algorithms. 相似文献
999.
Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic
control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance
measures of the actions are updated. The objective of the agent is to select the future best actions based on the updated
performance measures. In this paper, we develop an asynchronous action-reward learning model which updates the performance
measures of actions faster than conventional action-reward learning. This learning model is suitable to apply to nonstationary
control domain where the rewards for actions vary over time. Based on the asynchronous action-reward learning, two situation
reactive inventory control models (centralized and decentralized models) are proposed for a two-stage serial supply chain
with nonstationary customer demand. A simulation based experiment was performed to evaluate the performance of the proposed
two models.
Chang Ouk Kim received his Ph.D. in industrial engineering from Purdue University in 1996 and his B.S. and M.S. degrees from Korea University,
Republic of Korea in 1988 and 1990, respectively. From 1998--2001, he was an assistant professor in the Department of Industrial
Systems Engineering at Myongji University, Republic of Korea. In 2002, he joined the Department of Information and Industrial
Engineering at Yonsei University, Republic of Korea and is now an associate professor. He has published more than 30 articles
at international journals. He is currently working on applications of artificial intelligence and adaptive control theory
in supply chain management, RFID based logistics information system design, and advanced process control in semiconductor
manufacturing.
Ick-Hyun Kwon is a postdoctoral researcher in the Department of Civil and Environmental Engineering at University of Illinois at Urbana-Champaign.
Previous to this position, Dr. Kwon was a research assistant professor in the Research Institute for Information and Communication
Technology at Korea University, Seoul, Republic of Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering
from Korea University, in 1998, 2000, and 2006, respectively. His current research interests are supply chain management,
inventory control, production planning and scheduling.
Jun-Geol Baek is an assistant professor in the Department of Business Administration at Kwangwoon University, Seoul, Korea. He received
his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, Seoul, Korea, in 1993, 1995, and 2001 respectively.
From March 2002 to February 2007, he was an assistant professor in the Department of Industrial Systems Engineering at Induk
Institute of Technology, Seoul, Korea. His research interests include machine learning, data mining, intelligent machine diagnosis,
and ubiquitous logistics information systems.
An erratum to this article can be found at 相似文献
1000.
Sagar Chaki Edmund Clarke Natasha Sharygina Nishant Sinha 《Formal Methods in System Design》2008,32(3):235-266
This paper presents an automated and compositional procedure to solve the substitutability problem in the context of evolving software systems. Our solution contributes two
techniques for checking correctness of software upgrades: (1) a technique based on simultaneous use of over-and under-approximations
obtained via existential and universal abstractions; (2) a dynamic assume-guarantee reasoning algorithm—previously generated component assumptions are reused and altered on-the-fly to prove
or disprove the global safety properties on the updated system. When upgrades are found to be non-substitutable, our solution
generates constructive feedback to developers showing how to improve the components. The substitutability approach has been
implemented and validated in the ComFoRT reasoning framework, and we report encouraging results on an industrial benchmark.
This is an extended version of a paper, Dynamic Component Substitutability Analysis, published in the Proceedings of the Formal Methods 2005 Conference, Lecture Notes in Computer Science, vol. 3582, by the
same authors. This research was sponsored by the National Science Foundation under grant nos. CNS-0411152, CCF-0429120, CCR-0121547,
and CCR-0098072, the Semiconductor Research Corporation under grant no. TJ-1366, the US Army Research Office under grant no.
DAAD19-01-1-0485, the Office of Naval Research under grant no. N00014-01-1-0796, the ICAST project and the Predictable Assembly
from Certifiable Components (PACC) initiative at the Software Engineering Institute, Carnegie Mellon University. The views
and conclusions contained in this document are those of the authors and should not be interpreted as representing the official
policies, either expressed or implied, of any sponsoring institution, the US government or any other entity. 相似文献