Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Online adaptive decision trees.

Jayanta Basak1

  • 1IBM India Research Lab, Indian Institute of Technology, New Delhi-110048, India. bjayanta@in.ibm.com

Neural Computation
|July 22, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Multiple exemplar-based facial image retrieval using independent component analysis.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2006
Same author

Online adaptive decision trees: pattern classification and function approximation.

Neural computation·2006
Same author

A classification paradigm for distributed vertically partitioned data.

Neural computation·2004
Same author

Dynamical response of an excitatory-inhibitory neural network to external stimulation: an application to image segmentation.

Physical review. E, Statistical, nonlinear, and soft matter physics·2002
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Online adaptive decision trees (OADT) offer a novel approach to pattern classification, combining decision tree structure with neural network adaptivity. This new method demonstrates superior performance over existing models in real-world datasets.

Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Decision trees and neural networks are established pattern classification tools.
  • Decision trees offer localized representation but lack online learning and adaptivity.
  • Neural networks provide distributed representation and excel in online learning and adaptivity.

Purpose of the Study:

  • Introduce a novel classification scheme, online adaptive decision trees (OADT).
  • Develop OADT as a tree-structured network capable of online learning and environmental adaptation.
  • Derive a new objective measure for supervised learning within the OADT framework.

Main Methods:

  • Designed OADT as a hybrid model integrating decision tree structure with neural network learning capabilities.

Related Experiment Videos

  • Developed a new objective measure to facilitate supervised learning in OADT.
  • Conducted experiments using real-life datasets to evaluate OADT performance.

    • Main Results:

    • Validated the effectiveness of the proposed online adaptive decision trees (OADT) classification scheme.
    • Demonstrated that OADT outperforms hierarchical mixture of experts and multilayer perceptron models.
    • Confirmed OADT's capability for online learning and adaptation in dynamic environments.

    Conclusions:

    • Online adaptive decision trees (OADT) present a viable and effective alternative for pattern classification tasks.
    • OADT successfully bridges the gap between static decision trees and adaptive neural networks.
    • The proposed OADT scheme shows significant potential for applications requiring continuous learning and adaptation.