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Found 7 papers, 4 papers with code
Predicting the Impact of Batch Refactoring Code Smells on Application Resource Consumption
Although automated batch refactoring techniques are known to significantly improve overall software quality and maintainability, their impact on resource utilization is not well studied.
Scalable Manifold Learning for Big Data with Apache Spark
Non-linear spectral dimensionality reduction methods, such as Isomap, remain important technique for learning manifolds.
Fast Counting in Machine Learning Applications
We propose scalable methods to execute counting queries in machine learning applications.
Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes
Scientific and engineering processes deliver massive high-dimensional data sets that are generated as non-linear transformations of an initial state and few process parameters.
Scalable Exact Parent Sets Identification in Bayesian Networks Learning with Apache Spark
In Machine Learning, the parent set identification problem is to find a set of random variables that best explain selected variable given the data and some predefined scoring function.
Error Metrics for Learning Reliable Manifolds from Streaming Data
In this paper, we argue that a stable manifold can be learned using only a fraction of the stream, and the remaining stream can be mapped to the manifold in a significantly less costly manner.
Exact Structure Learning of Bayesian Networks by Optimal Path Extension
The problem of exact structure learning is to find a network structure that is optimal under certain scoring criteria.
