Scenario tree generation approaches using K-means and LP moment matching methods

A Heuristic for Moment-Matching Scenario Generation

Multistage K-Means Clustering for Scenario Tree Construction

In stochastic programming and decision analysis, an important issue consists in the approximate representation of the multidimensional stochastic underlying process in the form of scenario tree. This paper presents the approach to generate the multistage multidimensional scenario tree out of a set of scenario fans. For this purpose, the multistage K-means clustering algorithm is developed. The ...

Multi-stage scenario generation by the combined moment matching and scenario reduction method

We describe an opportunity to speed up multi-stage scenario generation and reduction using a combination of two well known methods: the moment matching method (Høyland and Wallace 2001) and the method for scenario reduction to approximately minimize a metric (Heitsch and Römish 2009). Our suggestions is to combine them rather than using them in serial by making use of a stage-wise approximation...

Automatic Wrapper Generation Using Tree Matching and Partial Tree Alignment

This paper is concerned with the problem of structured data extraction from Web pages. The objective of the research is to automatically segment data records in a page, extract data items/fields from these records and store the extracted data in a database. In this paper, we first introduce the extraction problem, and then discuss the main existing approaches and their limitations. After that, ...

Sampling-Based Speech Parameter Generation Using Moment-Matching Networks

This paper presents sampling-based speech parameter generation using moment-matching networks for Deep Neural Network (DNN)-based speech synthesis. Although people never produce exactly the same speech even if we try to express the same linguistic and para-linguistic information, typical statistical speech synthesis produces completely the same speech, i.e., there is no inter-utterance variatio...