Sparse Feature Selection Makes Batch Reinforcement Learning More Sample EfficientBotao HaoCsaba SzepesváriMengdi WangTor LattimoreYaqi Duan
11.1 Sparse feature selection, 视频播放量 13、弹幕量 0、点赞数 0、投硬币枚数 0、收藏人数 0、转发人数 0, 视频作者 bili_90837690466, 作者简介 ,相关视频:Introduction to Derived Algebraic Geometry (UC Berkeley) 2023,12.1 Semi-supervised learning,6.1 Supp
Deng, Z. T.,Hu, G. Y., Pan, Z. S., & Zhang, Y. Y. Kernel SparseFeature Selection Based on Semantics in Text Classification[J].Information Technology Journal, 2012, (11): 319-323.Deng, Zhantao,Hu, Guyu,Pan, Zhisong,Zhang, Yanyan.Kernel sparse feature selection based on ...
从给定的特征集合中选择出相关特征子集的过程,称为“特征选择”(feature selection)。 特征选择是一个重要的“数据预处理”(data preprocessing)过程,在现实机器学习任务中,获得数据之后通常先进行特征选择,此后再训练学习器,那么,为什么要进行特征选择呢? 首先,我们在现实任务中经常会遇到维数灾难问题,这是由于属性过多...
Deep sparse feature selection for computer aided endoscopy diagnosisYang Conga,b,n, Shuai Wanga,e, Ji Liub, Jun Caoc, Yunsheng Yangd, Jiebo LuobaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, ChinabDepartment of Computer Science, University of ...
•Spare feature selection method based on l2,1/2-matix norm is proposed.•Graph Laplacian based semi-supervised learning is exploited.•A effective algorithm for optimizing the objective function is introduced.•The convergence of the algorithm is proven.•Experiments demonstrate that the metho...
Sparse discriminative feature selectiondoi:10.1016/j.patcog.2014.10.021Joint feature selectionSparse representation based classificationDiscriminative learningAs sparse representation-based classifier (SRC) is developed, it has drawn more and more attentions in dimension reduction. In this paper, we introduce...
Detection and Feature Selection in Sparse Mixture Models. arXiv e-prints arXiv:1405.1478, May 2014.Verzelen, N. and Arias-Castro, E. (2014). Detection and feature selection in sparse mixture models. arXiv preprint arXiv:1405.1478.Verzelen, N. and Arias-Castro, E. (2014). Detection and ...
Feature selection is an effective way to solve this problem. Motivated by this, in the paper, we propose a sparse L q-norm least squares support vector machine (Lq-norm LS-SVM) with 0 < q < 1, where feature selection and prediction are performed simultaneously. Different from traditional ...
•We have proposed a hypergraph learning approach for feature selection, aimed at capturing higher order sample relations in sets of data.•The approach not only incorporates a robust hyperedge construction method, but also allows for the simultaneously learning of hyperedge weights and feature sel...