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Python Descriptors, 2nd Edition - Programmer Books

#artificialintelligence

Create descriptors and see ideas and examples of how to use descriptors effectively. In this short book, you'll explore descriptors in general, with a deep explanation of what descriptors are, how they work, and how they're used. Once you understand the simplicity of the descriptor protocol, the author delves into using and creating descriptors in practice, with plenty of tips, patterns, and real-world guidance. Because descriptors are inherently flexible, you'll work with multiple examples illustrating how to best take advantage of them. This second edition includes additions throughout, including new material covering the set_name_() descriptors, new and improved flowcharts to explain the inner workings of descriptors and a completely new chapter to address instance-level attributes, the easiest way to create descriptors correctly the first time.


Python Descriptors, 2nd Edition [PDF] - Programmer Books

#artificialintelligence

Create descriptors and see ideas and examples of how to use descriptors effectively. In this short book, you'll explore descriptors in general, with a deep explanation of what descriptors are, how they work, and how they're used. Once you understand the simplicity of the descriptor protocol, the author delves into using and creating descriptors in practice, with plenty of tips, patterns, and real-world guidance. Because descriptors are inherently flexible, you'll work with multiple examples illustrating how to best take advantage of them. This second edition includes additions throughout, including new material covering the set_name_() descriptors, new and improved flowcharts to explain the inner workings of descriptors, and a completely new chapter to address instance-level attributes, the easiest way to create descriptors correctly the first time.


R2D2: Reliable and Repeatable Detector and Descriptor

Neural Information Processing Systems

Interest point detection and local feature description are fundamental steps in many computer vision applications. Classical approaches are based on a detect-then-describe paradigm where separate handcrafted methods are used to first identify repeatable keypoints and then represent them with a local descriptor. Neural networks trained with metric learning losses have recently caught up with these techniques, focusing on learning repeatable saliency maps for keypoint detection or learning descriptors at the detected keypoint locations. In this work, we argue that repeatable regions are not necessarily discriminative and can therefore lead to select suboptimal keypoints. Furthermore, we claim that descriptors should be learned only in regions for which matching can be performed with high confidence.


Learning Image Descriptors with the Boosting-Trick

Neural Information Processing Systems

In this paper we apply boosting to learn complex non-linear local visual feature representations, drawing inspiration from its successful application to visual object detection. The main goal of local feature descriptors is to distinctively represent a salient image region while remaining invariant to viewpoint and illumination changes. This representation can be improved using machine learning, however, past approaches have been mostly limited to learning linear feature mappings in either the original input or a kernelized input feature space. While kernelized methods have proven somewhat effective for learning non-linear local feature descriptors, they rely heavily on the choice of an appropriate kernel function whose selection is often difficult and non-intuitive. We propose to use the boosting-trick to obtain a non-linear mapping of the input to a high-dimensional feature space.


Important Molecular Descriptors Selection Using Self Tuned Reweighted Sampling Method for Prediction of Antituberculosis Activity

arXiv.org Machine Learning

In this paper, a new descriptor selection method for selecting an optimal combination of important descriptors of sulfonamide derivatives data, named self tuned reweighted sampling (STRS), is developed. descriptors are defined as the descriptors with large absolute coefficients in a multivariate linear regression model such as partial least squares(PLS). In this study, the absolute values of regression coefficients of PLS model are used as an index for evaluating the importance of each descriptor Then, based on the importance level of each descriptor, STRS sequentially selects N subsets of descriptors from N Monte Carlo (MC) sampling runs in an iterative and competitive manner. In each sampling run, a fixed ratio (e.g. 80%) of samples is first randomly selected to establish a regresson model. Next, based on the regression coefficients, a two-step procedure including rapidly decreasing function (RDF) based enforced descriptor selection and self tuned sampling (STS) based competitive descriptor selection is adopted to select the important descriptorss. After running the loops, a number of subsets of descriptors are obtained and root mean squared error of cross validation (RMSECV) of PLS models established with subsets of descriptors is computed. The subset of descriptors with the lowest RMSECV is considered as the optimal descriptor subset. The performance of the proposed algorithm is evaluated by sulfanomide derivative dataset. The results reveal an good characteristic of STRS that it can usually locate an optimal combination of some important descriptors which are interpretable to the biologically of interest. Additionally, our study shows that better prediction is obtained by STRS when compared to full descriptor set PLS modeling, Monte Carlo uninformative variable elimination (MC-UVE).