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AAAI
1998
15 years 7 months ago
Learning to Predict User Operations for Adaptive Scheduling
Mixed-initiativesystemspresent the challengeof finding an effective level of interaction betweenhumans and computers. Machinelearning presents a promising approachto this problemi...
Melinda T. Gervasio, Wayne Iba, Pat Langley
171
Voted
ICML
2010
IEEE
15 years 7 months ago
Learning Temporal Causal Graphs for Relational Time-Series Analysis
Learning temporal causal graph structures from multivariate time-series data reveals important dependency relationships between current observations and histories, and provides a ...
Yan Liu 0002, Alexandru Niculescu-Mizil, Aurelie C...
CORR
2000
Springer
75views Education» more  CORR 2000»
15 years 6 months ago
A Learning Approach to Shallow Parsing
A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors ...
Marcia Muñoz, Vasin Punyakanok, Dan Roth, D...
TIT
1998
70views more  TIT 1998»
15 years 5 months ago
The Importance of Convexity in Learning with Squared Loss
We show that if the closureof a function class F under the metric induced by some probability distribution is not convex, then the sample complexity for agnostically learning F wi...
Wee Sun Lee, Peter L. Bartlett, Robert C. Williams...
167
Voted
TNN
1998
76views more  TNN 1998»
15 years 5 months ago
Multiobjective genetic algorithm partitioning for hierarchical learning of high-dimensional pattern spaces: a learning-follows-d
— In this paper, we present a novel approach to partitioning pattern spaces using a multiobjective genetic algorithm for identifying (near-)optimal subspaces for hierarchical lea...
Rajeev Kumar, Peter Rockett