Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Ulf Brefeld,Edward Curry,Neil Hurley Conference proceedings 201

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書(shū)目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋




書(shū)目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋學(xué)科排名

debris

: Probabilistic Lineup Evaluation Through Network Embeddingich stands for LINeup NETwork). . exploits the dynamics of a directed network that captures the performance of lineups during their matchups. The nodes of this network represent the different lineups, while an edge from node B to node A exists if lineup . has outperformed lineup .. We further annota

Hyperplasia

Improving Emotion Detection with Sub-clip Boostingion detection from voice promises to transform a wide range of applications, from adding emotional-awareness to voice assistants, to creating more sensitive robotic helpers for the elderly. Unfortunately, due to individual differences, emotion expression varies dramatically, making it a challenging

BALE

eucalyptus

From Empirical Analysis to Public Policy: Evaluating Housing Systems for Homeless Youthing assistance across the nation. Despite these efforts, the number of youth still homeless or unstably housed remains very high. Motivated by this fact, we initiate a first study to understand and analyze the current governmental housing systems for homeless youth. In this paper, we aim to provide

Abutment

Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Gtinct signals grew too large to be analyzed manually. During development of a car only subsets of such signals are relevant per analysis and functional group. Moreover, historical growth led to redundancies in signal specifications which need to be discovered. Both tasks can be solved through the di

committed

Counteract

澄清

Intent-Aware Audience Targeting for Ride-Hailing Services for a particular message) for ride-hailing services is demanding for marketing campaigns. In this paper, we describe the details of our deployed system for intent-aware audience targeting on Baidu Maps for ride-hailing services. The objective of the system is to predict user intent for requesting

剝皮

A Recurrent Neural Network Survival Model: Predicting Web User Return Time site. Essential to this is predicting . a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this