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排序方式: 共有3732条查询结果,搜索用时 15 毫秒
61.
Peter J. Clarke Author Vitae Djuradj Babich Author Vitae 《Journal of Systems and Software》2008,81(12):2269-2286
The transition from Java 1.4 to Java 1.5 has provided the programmer with more flexibility due to the inclusion of several new language constructs, such as parameterized types. This transition is expected to increase the number of class clusters exhibiting different combinations of class characteristics. In this paper we investigate how the number and distribution of clusters are expected to change during this transition. We present the results of an empirical study were we analyzed applications written in both Java 1.4 and 1.5. In addition, we show how the variability of the combinations of class characteristics may affect the testing of class members. 相似文献
62.
The statistical properties of the autoregressive (AR) distance between ARIMA processes are investigated. In particular, the asymptotic distribution of the squared AR distance and an approximation which is computationally efficient are derived. Moreover, the problem of time series clustering and classification is discussed and the performance of the AR distance is illustrated by means of some empirical applications. 相似文献
63.
Huimin Zhao 《Knowledge and Information Systems》2008,15(3):321-334
In real-world classification problems, different types of misclassification errors often have asymmetric costs, thus demanding cost-sensitive learning methods that attempt to minimize average misclassification cost rather than plain error rate. Instance weighting and post hoc threshold adjusting are two major approaches to cost-sensitive classifier learning. This paper compares the effects of these two approaches on several standard, off-the-shelf classification methods. The comparison indicates that the two approaches lead to similar results for some classification methods, such as Naïve Bayes, logistic regression, and backpropagation neural network, but very different results for other methods, such as decision tree, decision table, and decision rule learners. The findings from this research have important implications on the selection of the cost-sensitive classifier learning approach as well as on the interpretation of a recently published finding about the relative performance of Naïve Bayes and decision trees. 相似文献
64.
Boosting text segmentation via progressive classification 总被引:5,自引:4,他引:1
Eugenio Cesario Francesco Folino Antonio Locane Giuseppe Manco Riccardo Ortale 《Knowledge and Information Systems》2008,15(3):285-320
A novel approach for reconciling tuples stored as free text into an existing attribute schema is proposed. The basic idea
is to subject the available text to progressive classification, i.e., a multi-stage classification scheme where, at each intermediate stage, a classifier is learnt that analyzes the textual
fragments not reconciled at the end of the previous steps. Classification is accomplished by an ad hoc exploitation of traditional
association mining algorithms, and is supported by a data transformation scheme which takes advantage of domain-specific dictionaries/ontologies.
A key feature is the capability of progressively enriching the available ontology with the results of the previous stages
of classification, thus significantly improving the overall classification accuracy. An extensive experimental evaluation
shows the effectiveness of our approach. 相似文献
65.
Learning often occurs through comparing. In classification learning, in order to compare data groups, most existing methods compare either raw instances or learned classification rules against each other. This paper takes a different approach, namely conceptual equivalence, that is, groups are equivalent if their underlying concepts are equivalent while their instance spaces do not necessarily overlap and their rule sets do not necessarily present the same appearance. A new methodology of comparing is proposed that learns a representation of each group’s underlying concept and respectively cross-exams one group’s instances by the other group’s concept representation. The innovation is fivefold. First, it is able to quantify the degree of conceptual equivalence between two groups. Second, it is able to retrace the source of discrepancy at two levels: an abstract level of underlying concepts and a specific level of instances. Third, it applies to numeric data as well as categorical data. Fourth, it circumvents direct comparisons between (possibly a large number of) rules that demand substantial effort. Fifth, it reduces dependency on the accuracy of employed classification algorithms. Empirical evidence suggests that this new methodology is effective and yet simple to use in scenarios such as noise cleansing and concept-change learning. 相似文献
66.
Automatic Construction and Verification of Isotopy Invariants 总被引:1,自引:0,他引:1
Volker Sorge Andreas Meier Roy McCasland Simon Colton 《Journal of Automated Reasoning》2008,40(2-3):221-243
We extend our previous study of the automatic construction of isomorphic classification theorems for algebraic domains by
considering the isotopy equivalence relation. Isotopism is an important generalisation of isomorphism, and is studied by mathematicians in domains
such as loop theory. This extension was not straightforward, and we had to solve two major technical problems, namely, generating
and verifying isotopy invariants. Concentrating on the domain of loop theory, we have developed three novel techniques for
generating isotopic invariants, by using the notion of universal identities and by using constructions based on subblocks.
In addition, given the complexity of the theorems that verify that a conjunction of the invariants form an isotopy class,
we have developed ways of simplifying the problem of proving these theorems. Our techniques employ an interplay of computer
algebra, model generation, theorem proving, and satisfiability-solving methods. To demonstrate the power of the approach,
we generate isotopic classification theorems for loops of size 6 and 7, which extend the previously known enumeration results.
This work was previously beyond the capabilities of automated reasoning techniques.
The author’s work was supported by EPSRC MathFIT grant GR/S31099. 相似文献
67.
Jolita Ralyt Manfred A. Jeusfeld Per Backlund Harald Kühn Nicolas Arni-Bloch 《Information Systems》2008,33(7-8):754
Interoperability is a key property of enterprise applications, which is hard to achieve due to the large number of interoperating components and semantic heterogeneity. The inherent complexity of interoperability problems implies that there exists no silver bullet to solve them. Rather, the knowledge about how to solve wicked interoperability problems is hidden in the application cases that expose those problems. The paper addresses the question of how to organise and use method knowledge to resolve interoperability problems. We propose the structure of a knowledge-based system that can deliver situation-specific solutions, called method chunks. Situational Method Engineering promotes modularisation and formalisation of method knowledge in the form of reusable method chunks, which can be combined to compose a situation-specific method. The method chunks are stored in a method chunk repository. In order to cater for management and retrieval, we introduce an Interoperability Classification Framework, which is used to classify and tag method chunks and to assess the project situation in which they are to be used. The classification framework incorporates technical as well as business and organisational aspects of interoperability. This is an important feature as interoperability problems typically are multifaceted spanning multiple aspects. We have applied the approach to analyse an industry case from the insurance sector to identify and classify a set of method chunks. 相似文献
68.
Several researchers have recently investigated the connection between reinforcement learning and classification. We are motivated
by proposals of approximate policy iteration schemes without value functions, which focus on policy representation using classifiers
and address policy learning as a supervised learning problem. This paper proposes variants of an improved policy iteration
scheme which addresses the core sampling problem in evaluating a policy through simulation as a multi-armed bandit machine.
The resulting algorithm offers comparable performance to the previous algorithm achieved, however, with significantly less
computational effort. An order of magnitude improvement is demonstrated experimentally in two standard reinforcement learning
domains: inverted pendulum and mountain-car. 相似文献
69.
It is well recognized that the impact-acoustic emissions contain information that can indicate the presence of the adhesive defects in the bonding structures. In our previous papers, artificial neural network (ANN) was adopted to assess the bonding integrity of the tile–walls with the feature extracted from the power spectral density (PSD) of the impact-acoustic signals acting as the input of classifier. However, in addition to the inconvenience posed by the general drawbacks such as long training time and large number of training samples needed, the performance of the classic ANN classifier is deteriorated by the similar spectral characteristics between different bonding status caused by abnormal impacts. In this paper our previous works was developed by the employment of the least-squares support vector machine (LS-SVM) classifier instead of the ANN to derive a bonding integrity recognition approach with better reliability and enhanced immunity to surface roughness. With the help of the specially designed artificial sample slabs, experiments results obtained with the proposed method are provided and compared with that using the ANN classifier, demonstrating the effectiveness of the present strategy. 相似文献
70.
Michael Shneier Tommy Chang Tsai Hong Will Shackleford Roger Bostelman James S. Albus 《Autonomous Robots》2008,24(1):69-86
Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability
of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate
the terrain around them and build up an internal representation that enables them to navigate. This paper addresses the question
of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment
are traversable. The approach makes use of sensed information from range sensors (stereo or ladar), color cameras, and the
vehicle’s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection
into a local occupancy grid. The models include color and texture as well as traversability information obtained from an analysis
of the range data associated with the pixels. The models are learned without supervision, deriving their properties from the
geometry and the appearance of the scene.
The models are used to classify color images and assign traversability costs to regions. The classification does not use the
range or position information, but only color images. Traversability determined during the model-building phase is stored
in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color
images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and
how the system adapts to changing environments. Examples are shown from the implementation of this algorithm in the DARPA
Learning Applied to Ground Robots (LAGR) program, and an evaluation of the algorithm against human-provided ground truth is
presented.
相似文献
James S. AlbusEmail: |