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Open AccessJournal ArticleDOI

Instance-Based Learning Algorithms

TLDR
This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.
Abstract
Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several real-world databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.

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Citations
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Journal ArticleDOI

SEG-SSC: A Framework Based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification

TL;DR: This framework is designed to improve the classification performance of any given self-labeled method by using synthetic labeled data generated via an oversampling technique and a positioning adjustment model that use both labeled and unlabeled examples as reference.
Journal ArticleDOI

Regression via Classification applied on software defect estimation

TL;DR: Regression via Classification (RvC) is applied to the problem of estimating the number of software defects and manages to get better regression error than the standard regression approaches on both datasets.
Journal ArticleDOI

Error Detection in Spoken Human-Machine Interaction

TL;DR: It appears that people systematically use negative/marked cues when there are communication problems, and it is claimed that if spoken dialogue systems were able to detect such cues and change their strategy accordingly, the interaction between user and system would improve.
Posted Content

Robobarista: Object Part based Transfer of Manipulation Trajectories from Crowd-sourcing in 3D Pointclouds

TL;DR: In this article, the authors formulate the manipulation planning as a structured prediction problem and design a deep learning model that can handle large noise in the manipulation demonstrations and learns features from three different modalities: point-clouds, language and trajectory.
Proceedings ArticleDOI

An Empirical Study of the Domain Dependence of Supervised Word Disambiguation Systems

TL;DR: It is demonstrated empirically that the Lazy-Boosting algorithm outperforms state-of-the-art supervised WSD algorithms in both previous situations.
References
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Journal ArticleDOI

Induction of Decision Trees

J. R. Quinlan
- 25 Mar 1986 - 
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Book

Classification and regression trees

Leo Breiman
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Journal ArticleDOI

Nearest neighbor pattern classification

TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.