Boosting and Additive Trees

2008 ◽  
pp. 1-51 ◽  
Author(s):  
Trevor Hastie ◽  
Robert Tibshirani ◽  
Jerome Friedman
Keyword(s):  
Additive Trees

scholarly journals Minimum description length and psychological clustering models

2019 ◽  
Author(s):  
Michael David Lee ◽  
Danielle Navarro
Keyword(s):  
Data Analysis ◽  
Goodness Of Fit ◽  
Minimum Description Length ◽  
Additive Clustering ◽  
Similarity Data ◽  
Additive Trees ◽  
Using Data

Clustering is one of the most basic and useful methods of data analysis. This chapter describes a number of powerful clustering models, developed in psychology, for representing objects using data that measure the similarities between pairs of objects. These models place few restrictions on how objects are assigned to clusters,and allow for very general measures of the similarities between objects and clusters.Geometric Complexity Criteria (GCC) are derived for these models, and are used to fit the models to similarity data in a way that balances goodness-of-fit with complexity. Complexity analyses, based on the GCC, are presented for the two most widely used psychological clustering models, known as “additive clustering”and “additive trees”

Radical right parties and European economic integration: Evidence from the seventh European Parliament

2018 ◽  
Vol 19 (2) ◽  
pp. 321-343 ◽  
Author(s):  
Matteo Cavallaro ◽  
David Flacher ◽  
Massimo Angelo Zanetti
Keyword(s):  
European Parliament ◽  
Two Dimensions ◽  
Radical Right ◽  
Future Research ◽  
The European Union ◽  
Additive Trees ◽  
Radical Right Parties ◽  
Party Family ◽  
Clustering Criterion

This article explores the differences in radical right parties' voting behaviour on economic matters at the European Parliament. As the literature highlights the heterogeneity of these parties in relation to their economic programmes, we test whether divergences survive the elections and translate into dissimilar voting patterns. Using voting records from the seventh term of the European Parliament, we show that radical right parties do not act as a consolidated party family. We then analyse the differences between radical right parties by the means of different statistical methods (NOMINATE, Ward's clustering criterion, and additive trees) and find that these are described along two dimensions: the degree of opposition to the European Union and the classical left–right economic cleavage. We provide a classification of these parties compromising four groups: pro-welfare conditional, pro-market conditional, and rejecting. Our results indicate that radical right parties do not act as a party family at the European Parliament. This remains true regardless of the salience of the policy issues in their agendas. The article also derives streams for future research on the heterogeneity of radical right parties.

Constructing Additive Trees When the Error Is Small

1998 ◽  
Vol 5 (1) ◽  
pp. 127-133 ◽  
Author(s):  
LUSHENG WANG ◽  
DAN GUSFIELD
Keyword(s):  
Additive Trees

scholarly journals Improving circuit performance with multispeculative additive trees in high-level synthesis

2014 ◽  
Vol 45 (11) ◽  
pp. 1470-1479 ◽  
Author(s):  
Alberto A. Del Barrio ◽  
Román Hermida ◽  
Seda Ogrenci Memik ◽  
José M. Mendías ◽  
María C. Molina
Keyword(s):  
High Level Synthesis ◽  
Circuit Performance ◽  
Additive Trees ◽  
High Level

Mapping brand similarities: Comparing consumer online comments versus survey data

2018 ◽  
Vol 61 (2) ◽  
pp. 130-139 ◽  
Author(s):  
Marco Vriens ◽  
Song Chen ◽  
Chad Vidden
Keyword(s):  
Hierarchical Clustering ◽  
Online Forums ◽  
Online Data ◽  
Online Consumer Behavior ◽  
Similarity Data ◽  
Scaling Methods ◽  
Additive Trees ◽  
Online Consumer ◽  
The Cost ◽  
Brand Similarity

Online consumer behavior has become a valuable and viable source of consumer insights. Consumer comments in online forums, or discussion groups, have proven useful as a source to extract brand similarity data from. Apart from the cost and speed advantages, such data can be captured easily over different time periods. Both online consumer-generated data (CGD) and surveys have their pros and cons. To date, little is known as to how these two data sources compare in terms of brand insights. In this study, we discuss the results from analyzing survey and consumer-generated online data pertaining to the U.S. skincare market. Our study included 57 brands, and we used multidimensional scaling (MDS), t-stochastic neighbor embedding (t-SNE; an alternative to MDS), hierarchical clustering, and additive similarity trees (an extension of hierarchical clustering) to analyze the data. We show that the outcomes vary between CGD and surveys. As an additional insight, we show that, rather than the spatial scaling methods, additive trees result in a much better fit of brand similarity data in cases where we have many brands.

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