machine-learning

Process Sensitivity Analysis for Ecological Modeling

Borrett, S.R., Bridewell, W., Langley, P., & Arrigo, K.R. (2006). Process sensitivity analysis for ecological modeling. The Fifth International Conference on Ecological Informatics. Santa Barbara, CA.

Inductive Process Modeling

Developing approaches for learning models of nonlinear, dynamic systems from time-series data.

Integrated Systems for Inducing Spatio-temporal Process Models

Integrated Systems for Inducing Spatio-temporal Process Models

Quantitative modeling plays a key role in the natural sciences, and systems that address the task of inductive process modeling can assist researchers in explaining their data. In the past, such systems have been limited to data sets that recorded …

The Induction and Transfer of Declarative Bias

People constantly apply acquired knowledge to new learning tasks, but machines almost never do. Research on transfer learning attempts to address this dissimilarity. Working within this area, we report on a procedure that learns and transfers …

Processes and Constraints in Scientific Model Construction

In previous publications, we have reported a computational approach to constructing quantitative process models of dynamic systems from time-series data and background knowledge. However, our experience with these systems suggests that process …

Extracting Constraints for Process Modeling

In this paper, we introduce an approach for extracting constraints on process model construction. We begin by clarifying the type of knowledge produced by our method and how one may apply it. Next, we review the task of inductive process modeling, …

Learning Declarative Bias

In this paper, we introduce an inductive logic programming approach to learning declarative bias. The target learning task is inductive process modeling, which we briefly review. Next we discuss our approach to bias induction while emphasizing …

Learning Process Models with Missing Data

In this paper, we review the task of inductive process modeling, which uses domain knowledge to compose explanatory models of continuous dynamic systems. Next we discuss approaches to learning with missing values in time series, noting that these …

Reducing Overfitting in Process Model Induction

In this paper, we review the paradigm of inductive process modeling, which uses background knowledge about possible component processes to construct quantitative models of dynamical systems. We note that previous methods for this task tend to overfit …