In recent years, evolutionary and other nature-inspired algorithms have become human competitors in designing CNN and other deep networks automatically. However, one challenge for these methods is their very high computational cost. In this chapter, we investigate if we can find an optimized CNN ...
这个特性是我们所希望的。 顺便说一下,如何降低最近邻分类器的计算复杂度也是研究的热门,有几种近似最近邻算法(Approximate Nearest Neighbor (ANN) algorithms)能够加速在数据集中查找最近邻的效率(如FLANN),这些方法通常在预处理阶段建立kd树或使用k-means聚类等,它们也是牺牲了一定的最近邻精度来换取空间/时间复杂度...
[65] Y. LeCun et al., “Learning algorithms for classification: A comparison on handwritten digit recognition,” Neural networks Stat. Mech. Perspect., vol. 261, p. 276, 1995.[66] T. Joachims, “Text categorization with support vector machines: Learning with many relevant features,” in E...
The pre-trained DenseNet201 network is used for CNN feature extraction from each task-specific handwritten image data. The extracted CNN features are then fused in different combinations. Three machine learning algorithms support vector machine (SVM), AdaBoost, and Random forest are employed to ...
tumor segmentation and classification algorithms[J/OL]. J Imaging, 2021,7(9):179[2022-08-15].https://doi.org/10.3390/jimaging7090179. DOI:10.3390/jimaging7090179. [3] NAZIR M,SHAKIL S,KHURSHID K.Role of deep learning in brain tumor det...
[65] Y. LeCun et al., “Learning algorithms for classification: A comparison on handwritten digit recognition,” Neural networks Stat. Mech. Perspect., vol. 261, p. 276, 1995.[66] T. Joachims, “Text categorization with support vector machines: Learning with many relevant features,” in ...
In this, the work exhibits about working of the Convolutional Neural Networks (CNNs) for image classification. Deep learning approaches are better than the traditional learning algorithms when the data size is large because every day, a vast volume of data is accumulated everywhere. In deep ...
E. Hinton. Imagenetclassification with deep convolutional neural networks. InAdvances in Neural Information Processing Systems, pages1097–1105, 2012. 2[38] A. Levin, Y. Weiss, F. Durand, and W. T. Freeman. Understanding and evaluating blind deconvolution algorithms. InIEEE Conference on ...
In recent years great development has been observed in artificial intelligence reason being the deep learning algorithms (Rehman and Chong, 2020). The best part of Deep Neural Network (DNN) is that it extracts the features on its own; therefore, we don’t need to try optimal feature extractor...
Aerial scene classification purposes to automatically label aerial images with specific semantic categories. However, cataloguing presents a fundamental problem for high-resolution remote-sensing imagery (HRRS). Recent developments include several approaches and numerous algorithms address the task. This ...