Enhancing Predictive Modeling of Nested Spatial Data through Group-Level Feature Disaggregation
Authors:
Boyang Liu (Michigan State University); Pang-Ning Tan (Michigan State University); Jiayu Zhou (Michigan State University)
More on http://www.kdd.org/kdd2018/
Видео Enhancing Predictive Modeling of Nested Spatial Data through Group-Level Feature Disaggregation канала KDD2018 video
Boyang Liu (Michigan State University); Pang-Ning Tan (Michigan State University); Jiayu Zhou (Michigan State University)
More on http://www.kdd.org/kdd2018/
Видео Enhancing Predictive Modeling of Nested Spatial Data through Group-Level Feature Disaggregation канала KDD2018 video
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Online Parameter Selection for Web-based Ranking ProblemsDeploying Machine Learning Models for Public Policy: A FrameworkTowards Explanation of DNN-based Prediction with Guided Feature InversionRisk Prediction on Electronic Healthcare Records with Prior Medical KnowledgeFairness of Exposure in RankingsNotification Volume Control and Optimization System at PinterestMultimodal Sentiment Analysis To Explore the Structure of EmotionsTax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning ApproachScalable Spectral Clustering Using Random Binning FeaturesEvoGraph: An Effective and Efficient Graph Upscaling Method for Preserving Graph PropertiesSimultaneous Urban Region Function Discovery and Popularity Estimation Via an Infinite UrbanizationPredicting Estimated Time of Arrival for Commercial FlightsAnatomy of a Privacy-Safe Large-Scale Information Extraction System Over EmailA Data-Driven Three-Layer Algorithm for Split Delivery Vehicle Routing ProblemDisturbance Grassmann Kernels for Subspace-Based LearningAn Extensible Event Extraction System With Cross-Media Event ResolutionHierarchical Taxonomy Aware Network EmbeddingTowards Mitigating the Class-Imbalance Problem for Partial Label LearningHeavyGuardian: Separate and Guard Hot Items in Data StreamsRanking Distillation: Learning Compact Ranking Models With High Performance for Recommender System