Sciweavers

ML
2006
ACM
110views Machine Learning» more  ML 2006»
13 years 10 months ago
Classification-based objective functions
Backpropagation, similar to most learning algorithms that can form complex decision surfaces, is prone to overfitting. This work presents classification-based objective functions, ...
Michael Rimer, Tony Martinez
ML
2006
ACM
131views Machine Learning» more  ML 2006»
13 years 10 months ago
Markov logic networks
We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge b...
Matthew Richardson, Pedro Domingos
ML
2006
ACM
110views Machine Learning» more  ML 2006»
13 years 10 months ago
Distribution-based aggregation for relational learning with identifier attributes
Abstract Identifier attributes--very high-dimensional categorical attributes such as particular product ids or people's names--rarely are incorporated in statistical modeling....
Claudia Perlich, Foster J. Provost
ML
2006
ACM
13 years 10 months ago
Universal parameter optimisation in games based on SPSA
Most game programs have a large number of parameters that are crucial for their performance. While tuning these parameters by hand is rather difficult, efficient and easy to use ge...
Levente Kocsis, Csaba Szepesvári
ML
2006
ACM
13 years 10 months ago
A Unified View on Clustering Binary Data
Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This p...
Tao Li
ML
2006
ACM
163views Machine Learning» more  ML 2006»
13 years 10 months ago
Extremely randomized trees
Abstract This paper proposes a new tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute ...
Pierre Geurts, Damien Ernst, Louis Wehenkel
ML
2006
ACM
122views Machine Learning» more  ML 2006»
13 years 10 months ago
PRL: A probabilistic relational language
In this paper, we describe the syntax and semantics for a probabilistic relational language (PRL). PRL is a recasting of recent work in Probabilistic Relational Models (PRMs) into ...
Lise Getoor, John Grant
ML
2006
ACM
113views Machine Learning» more  ML 2006»
13 years 10 months ago
Learning to bid in bridge
Bridge bidding is considered to be one of the most difficult problems for game-playing programs. It involves four agents rather than two, including a cooperative agent. In additio...
Asaf Amit, Shaul Markovitch
ML
2006
ACM
13 years 10 months ago
Type-sensitive control-flow analysis
Higher-order typed languages, such as ML, provide strong support for data and type abn. While such abstraction is often viewed as costing performance, there are situations where i...
John H. Reppy