George H. John

Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

EDBT 2026 Demo / reviewers in the wild / expert

dblp:82/6804 · DBLP profile ↗

← Back

12ranked-venue papers

9as first author

0since 2021 · last 1997

—ORCID · none

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

Artificial intelligence and machine learning · 10 · 7 first-authorDatabases, data management, data science and information retrieval · 3 · 3 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-authorApplied, interdisciplinary, general and emerging computing · 2 · 2 first-author

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 4 papers		Representation and self-supervised learning · 25% Learning theory · 24% Optimization for machine learning · 19%
Databases, data mining, and information retrieval 3 papers		Data mining · 46% Data stream processing · 29% Query processing and optimization · 25%

Topics — the 10 heaviest of 11, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction feature selection	1	1997	Wrappers for Feature Subset Selection · Artif. Intell. 1997
Data stream processing stream processing systems	1	1997	SIPping from the Data Firehose · KDD 1997
Query processing and optimization › approximate query processing dynamic sampling	1	1996	Static Versus Dynamic Sampling for Data Mining · KDD 1996
Data mining sampling	1	1996	Static Versus Dynamic Sampling for Data Mining · KDD 1996
Machine learning › Optimization for machine learning hyperparameter optimization	1	1995	Automatic Parameter Selection by Minimizing Estimated Error · ICML 1995
Machine learning › Learning theory › model selection parameter selection	1	1995	Automatic Parameter Selection by Minimizing Estimated Error · ICML 1995
Data mining › anomaly detection outlier detection	1	1995	Robust Decision Trees: Removing Outliers from Databases · KDD 1995
Machine learning › Kernel, tree and ensemble methods decision tree	1	1994	Finding Multivariate Splits in Decision Trees Using Function Optimization · AAAI 1994
Machine learning › Reinforcement learning › value-based reinforcement learning q-learning	1	1994	When the Best Move Isn't Optimal: Q-learning with Exploration · AAAI 1994
Machine learning › Learning theory › statistical estimation error estimation	1	1995	Automatic Parameter Selection by Minimizing Estimated Error · ICML 1995

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

wrapper methods · 0.0feature selection · 0.0data stream processing · 0.0data mining · 0.0estimated error minimization · 0.0decision tree · 0.0q-learning · 0.0function optimization · 0.0

Year	Publication	Venue	Position
1997	Mortgage data mining abstract The paper reports a preliminary investigation of the use of of modern data mining tools for mortgage scoring. Using IBM's Intelligent Miner (a data mining toolbox), the authors built a model of serious delinquency on a sample of data from Mortgage Information Corporation's Loan Performance System, which contains over 20 million loans with a volume of over $1.6 trillion. Currently, two technologies prevail in mortgage scoring: logistic regression, a very old and very simple method, and neural networks, newer and more complex types of models that can be extremely difficult to interpret. The radial basis function (RBF) algorithm in Intelligent Miner combines the mathematical complexity and generality of neural networks with a comprehensible visualization that explains the RBF model. Due to the performance and understandability of the RBF model, as well as other unique technologies not described, the Intelligent Miner should be a useful tool for mortgage bankers, facilitating development of customized systems for mortgage scoring and other mortgage banking applications. George H. John, Yin Zhao	CIFEr	1
1997	SIPping from the Data Firehose George H. John, Brian Lent	KDD	1
1997	Wrappers for Feature Subset Selection Ron Kohavi, George H. John	Artif. Intell.	2
1996	Building long/short portfolios using rule induction abstract We approach stock selection for long/short portfolios from the perspective of knowledge discovery in databases and rule induction: given a database of historical information on some universe of stocks, discover rules from the data that will allow one to predict which stocks are likely to have exceptionally high or low returns in the future. Long/short portfolios allow a fund manager to independently address value-added stock selection and factor exposure, and are a popular tool in financial engineering. For stock selection we employed the Recon system, which is able to induce a set of rules to model the data it is given. We evaluate Recon's stock selection performance by using it to build equitized long/short portfolios over eighteen quarters of historical data from October 1988 to March 1993, repeatedly using the previous four quarters of data to build a model which is then used to rank stocks in the current quarter. When trading costs were taken into account, Recon's equitized long/short portfolio had a total return of 277%, significantly outperforming the benchmark (S&P500), which returned 92.5% over the same period. We conclude that rule induction is a valuable tool for stock selection. George H. John, Peter Miller	CIFEr	1
1996	Static Versus Dynamic Sampling for Data Mining George H. John, Pat Langley	KDD	1
1995	Automatic Parameter Selection by Minimizing Estimated Error Ron Kohavi, George H. John	ICML	2
1995	Robust Decision Trees: Removing Outliers from Databases George H. John	KDD	1
1995	Estimating Continuous Distributions in Bayesian Classifiers George H. John, Pat Langley	UAI	1
1994	Finding Multivariate Splits in Decision Trees Using Function Optimization George H. John	AAAI	1
1994	When the Best Move Isn't Optimal: Q-learning with Exploration George H. John	AAAI	1
1994	Irrelevant Features and the Subset Selection Problem George H. John, Ron Kohavi, Karl Pfleger	ICML	1
1994	MLC++: A Machine Learning Library in C++ abstract We present MLC++, a library of C++ classes and tools for supervised machine learning. While MLC++ provides general learning algorithms that can be used by end users, the main objective is to provide researchers and experts with a wide variety of tools that can accelerate algorithm development, increase software reliability, provide comparison tools, and display information visually. More than just a collection of existing algorithms, MLC++ is can attempt to extract commonalities of algorithms and decompose them for a unified view that is simple, coherent, and extensible. In this paper we discuss the problems MLC++ aims to solve, the design of MLC++, and the current functionality.> Ron Kohavi, George H. John, Richard Long, David Manley, Karl Pfleger	ICTAI	2