A Theoretical Analysis of Feature Fusion in Stacked Generalization

Date

2009-04-11

Author

Ozay, Mete
Yarman Vural, Fatoş Tunay

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

133
views

0
downloads

In the present work, a theoretical framework in order to define the general performance of stacked generalization learning algorithm is developed. Analytical relationships between the performance of the Stacked Generalization classifier relative to the individual classifiers are constructed by the proposed theorems and the practical techniques are developed in order to optimize the performance of stacked generalization algorithm based on these relationships.

URI

https://hdl.handle.net/11511/53537

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

A pattern classification approach for boosting with genetic algorithms Yalabık, Ismet; Yarman Vural, Fatoş Tunay; Üçoluk, Göktürk; Şehitoğlu, Onur Tolga (2007-11-09) Ensemble learning is a multiple-classifier machine learning approach which produces collections and ensembles statistical classifiers to build up more accurate classifier than the individual classifiers. Bagging, boosting and voting methods are the basic examples of ensemble learning. In this study, a novel boosting technique targeting to solve partial problems of AdaBoost, a well-known boosting algorithm, is proposed. The proposed system finds an elegant way of boosting a bunch of classifiers successively ...
A comparative study on ILP-based concept discovery systems Kavurucu, Yusuf; Karagöz, Pınar; Toroslu, İsmail Hakkı (2011-09-01) Inductive Logic Programming (ILP) studies learning from examples, within the framework provided by clausal logic. ILP has become a popular subject in the field of data mining due to its ability to discover patterns in relational domains. Several ILP-based concept discovery systems are developed which employs various search strategies, heuristics and language pattern limitations. LINUS, GOLEM, CIGOL, MIS, FOIL, PROGOL, ALEPH and WARMR are well-known ILP-based systems. In this work, firstly introductory infor...
A practical analysis of sample complexity for structure learning of discrete dynamic Bayesian networks Geduk, Salih; Ulusoy, İlkay (2021-02-01) Discrete Dynamic Bayesian Network (dDBN) is used in many challenging causal modelling applications, such as human brain connectivity, due to its multivariate, non-deterministic, and nonlinear capability. Since there is not a ground truth for brain connectivity, the resulting model cannot be evaluated quantitatively. However, we should at least make sure that the best structure results for the used modelling approach and the data. Later, this result can be appreciated by further correlated literature of anat...
A Framework for Machine Vision based on Neuro-Mimetic Front End Processing and Clustering Akbaş, Emre; ECKSTEIN, Miguel; MADHOW, Upamanyu (2014-10-03) Convolutional deep neural nets have emerged as a highly effective approach for machine vision, but there are a number of open issues regarding training (e.g., a large number of model parameters to be learned, and a number of manually tuned algorithm parameters) and interpretation (e.g., geometric interpretations of neurons at various levels of the hierarchy). In this paper, our goal is to explore alternative convolutional architectures which are easier to interpret and simpler to implement. In particular, w...
A visual interactive approach for scenario-based stochastic multi-objective problems and an application Balibek, E.; Köksalan, Mustafa Murat (2012-12-01) In many practical applications of stochastic programming, discretization of continuous random variables in the form of a scenario tree is required. In this paper, we deal with the randomness in scenario generation and present a visual interactive method for scenario-based stochastic multi-objective problems. The method relies on multi-variate statistical analysis of solutions obtained from a multi-objective stochastic problem to construct joint confidence regions for the objective function values. The decis...

Citation Formats

M. Ozay and F. T. Yarman Vural, “A Theoretical Analysis of Feature Fusion in Stacked Generalization,” 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53537.