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Guohua Hao

dblp:29/6892 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-authorDatabases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Learning theory · 77% Language models and text generation · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning theory › classification › multiclass classification
output coding		 0.112007
Revisiting Output Coding for Sequential Supervised Learning · IJCAI 2007

Methods — techniques the papers use, named apart from their topics

output coding · 0.1
YearPublicationVenuePosition
2020 Predicting Escalations in Customer Support: Analysis of Data Mining Challenge Results
abstract
We summarize IEEE Big Data Cup: Predicting Escalations in Customer Support - a data mining competition organized jointly by companies Information Builders and QED Software at the KnowledgePit platform, in the frame of the 2020 IEEE International Conference on Big Data. We discuss the motivation for organizing this event and highlight the factors that make it such a challenging topic. We describe the data provided to participants and formulate the competition task. We also provide an overview of competition results with a detailed analysis of a few selected solutions. Finally, we present a novel functionality of the KnowledgePit platform - an analytic module that allows organizers to investigate selected solutions using a convenient GUI and provides in-depth insights about their quality.
Andrzej Janusz, Guohua Hao, Daniel Kaluza, Tony Li, Robert Wojciechowski, Dominik Slezak
 IEEE BigData2
2007 Revisiting Output Coding for Sequential Supervised Learning
Guohua Hao, Alan Fern
 IJCAI1