Supplementary Materials for Dual Set Multi-Label Learning
نویسندگان
چکیده
Chong Liu, Peng Zhao, Sheng-Jun Huang, Yuan Jiang, Zhi-Hua Zhou 1 National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2 Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China 3 College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China {liuc, zhaop, jiangy, zhouzh}@lamda.nju.edu.cn, [email protected]
منابع مشابه
Supplementary : Extreme Multi-label Learning with Label Features for Warm-start Tagging, Ranking & Recommendation
Section 1 presents the pseudocodes for SwiftXML training and prediction algorithms. Section 2 reports complete set of experimental results comparing SwiftXML to various baselines in terms of both propensity-scored precisions (PSP1,PSP3,PSP5) as well as standard precisions (P1,P3,P5). Section 3 shows the derivations for individual steps of the alternating minimization algorithm used for node par...
متن کاملMLIFT: Enhancing Multi-label Classifier with Ensemble Feature Selection
Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific ...
متن کاملML-KNN: A lazy learning approach to multi-label learning
Multi-label learning originated from the investigation of text categorization problem, where each document may belong to several predefined topics simultaneously. In multi-label learning, the training set is composed of instances each associated with a set of labels, and the task is to predict the label sets of unseen instances through analyzing training instances with known label sets. In this...
متن کاملGeneralized hierarchical kernel learning
This paper generalizes the framework of Hierarchical Kernel Learning (HKL) and illustrates its utility in the domain of rule learning. HKL involves Multiple Kernel Learning over a set of given base kernels assumed to be embedded on a directed acyclic graph. This paper proposes a two-fold generalization of HKL: the first is employing a generic `1/`ρ block-norm regularizer (ρ ∈ (1, 2]) that allev...
متن کاملLarge-Scale Multi-Label Learning with Incomplete Label Assignments
Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlations. It is usually assumed, explicitly or implicitly, that the label sets for training instances are fully labeled without any missing labels. However, in many real-world multi-...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017