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1.
An Improved Gradient Projection-based Decomposition Technique for Support Vector Machines 总被引:2,自引:0,他引:2
Luca Zanni 《Computational Management Science》2006,3(2):131-145
In this paper we propose some improvements to a recent decomposition technique for the large quadratic program arising in
training support vector machines. As standard decomposition approaches, the technique we consider is based on the idea to
optimize, at each iteration, a subset of the variables through the solution of a quadratic programming subproblem. The innovative
features of this approach consist in using a very effective gradient projection method for the inner subproblems and a special
rule for selecting the variables to be optimized at each step. These features allow to obtain promising performance by decomposing
the problem into few large subproblems instead of many small subproblems as usually done by other decomposition schemes. We
improve this technique by introducing a new inner solver and a simple strategy for reducing the computational cost of each
iteration. We evaluate the effectiveness of these improvements by solving large-scale benchmark problems and by comparison
with a widely used decomposition package. 相似文献
2.
Huseyin Ince 《Computational Management Science》2006,3(2):161-174
The nature of the financial time series is complex, continuous interchange of stochastic and deterministic regimes. Therefore,
it is difficult to forecast with parametric techniques. Instead of parametric models, we propose three techniques and compare
with each other. Neural networks and support vector regression (SVR) are two universally approximators. They are data-driven
non parametric models. ARCH/GARCH models are also investigated. Our assumption is that the future value of Istanbul Stock
Exchange 100 index daily return depends on the financial indicators although there is no known parametric model to explain
this relationship. This relationship comes from the technical analysis. Comparison shows that the multi layer perceptron networks
overperform the SVR and time series model (GARCH). 相似文献
3.
梅家骝 《高校应用数学学报(A辑)》1989,4(4):541-549
本文讨论了笔者在[1]中提出的伪凸集,拟凸集的支撑函数与障碍锥的性质,并通过这些性质得出了二个闭性准则。 相似文献
4.
本文我们利用一个可微函数给出了一对高阶对称规划问题 ,其中目标函数包含了Rn 中一紧凸集的支撑函数 .在引入高阶F 凸性 (F 伪凸性 ,F 拟凸性 )后 ,证明了高阶弱、高阶强及高阶逆对称对偶性质 . 相似文献
5.
In this paper we construct the linear support vector machine (SVM) based on the nonlinear rescaling (NR) methodology (see
[Polyak in Math Program 54:177–222, 1992; Polyak in Math Program Ser A 92:197–235, 2002; Polyak and Teboulle in Math Program
76:265–284, 1997] and references therein). The formulation of the linear SVM based on the NR method leads to an algorithm
which reduces the number of support vectors without compromising the classification performance compared to the linear soft-margin
SVM formulation. The NR algorithm computes both the primal and the dual approximation at each step. The dual variables associated
with the given data-set provide important information about each data point and play the key role in selecting the set of
support vectors. Experimental results on ten benchmark classification problems show that the NR formulation is feasible. The
quality of discrimination, in most instances, is comparable to the linear soft-margin SVM while the number of support vectors
in several instances were substantially reduced. 相似文献
6.
Wanpracha Art Chaovalitwongse Oleg A. Prokopyev Panos M. Pardalos 《Annals of Operations Research》2006,148(1):227-250
Epilepsy is among the most common brain disorders. Approximately 25–30% of epilepsy patients remain unresponsive to anti-epileptic
drug treatment, which is the standard therapy for epilepsy. In this study, we apply optimization-based data mining techniques
to classify the brain's normal and epilepsy activity using intracranial electroencephalogram (EEG), which is a tool for evaluating
the physiological state of the brain. A statistical cross validation and support vector machines were implemented to classify
the brain's normal and abnormal activities. The results of this study indicate that it may be possible to design and develop
efficient seizure warning algorithms for diagnostic and therapeutic purposes.
Research was partially supported by the Rutgers Research Council grant-202018, the NSF grants DBI-980821, CCF-0546574, IIS-0611998,
and NIH grant R01-NS-39687-01A1. 相似文献
7.
8.
Formylation is one of the newly discovered post-translational modifications in lysine residue which is responsible for different kinds of diseases. In this work, a novel predictor, named predForm-Site, has been developed to predict formylation sites with higher accuracy. We have integrated multiple sequence features for developing a more informative representation of formylation sites. Moreover, decision function of the underlying classifier have been optimized on skewed formylation dataset during prediction model training for prediction quality improvement. On the dataset used by LFPred and Formator predictor, predForm-Site achieved 99.5% sensitivity, 99.8% specificity and 99.8% overall accuracy with AUC of 0.999 in the jackknife test. In the independent test, it has also achieved more than 97% sensitivity and 99% specificity. Similarly, in benchmarking with recent method CKSAAP_FormSite, the proposed predictor significantly outperformed in all the measures, particularly sensitivity by around 20%, specificity by nearly 30% and overall accuracy by more than 22%. These experimental results show that the proposed predForm-Site can be used as a complementary tool for the fast exploration of formylation sites. For convenience of the scientific community, predForm-Site has been deployed as an online tool, accessible at http://103.99.176.239:8080/predForm-Site. 相似文献
9.
In the analysis of gene expression profiles, the number of tissue samples with genes expression levels available is usually small compared with the number of genes. This can lead either to possible overfitting or even to a complete failure in analysis of microarray data. The selection of genes that are really indicative of the tissue classification concerned is becoming one of the key steps in microarray studies. In the present paper, we have combined the modified discrete particle swarm optimization (PSO) and support vector machines (SVM) for tumor classification. The modified discrete PSO is applied to select genes, while SVM is used as the classifier or the evaluator. The proposed approach is used to the microarray data of 22 normal and 40 colon tumor tissues and showed good prediction performance. It has been demonstrated that the modified PSO is a useful tool for gene selection and mining high dimension data. 相似文献