Noise Tolerant Inductive Learning
نویسندگان
چکیده
A novel iterative noise reduction learning algorithm is presented in which rules are learned in two phases. The ftrst phase improves the quality of training data through concept-driven closed-loop filtration process. In the second phase, classification rules are relearned from the filtered training dataset.
منابع مشابه
LEARNING TO RECOGNIZE VISUAL CONCEPTS: Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning
LEARNING TO RECOONIZE VISUAL CONCEPTS: DEVELOPMENf AND IMPLEMENTATION OF A METIIOD FOR TEXTURE CONCEPTS ACQUISITION TIIROUGH INDUCTIVE LEARNING JERZY WOJCIECH BALA, Ph.D. George Mason University, May 1993 Dissertation Director: Dr. Ryszard S. Michalski The goal of this research is to explore the application of symbolic learning methods to problems of computer vision. The research presented in t...
متن کاملA Self-Organizing Neural Network that Learns to Detect and Represent Visual Depth from Occlusion Events
nus paper discusses issues in noise tolerant learning from sensory data. A model driven approach to symbolic learning from noisy data is suggested. Introduction Sensor-driven characteristics of visual objects are rarely noise free and most often quite noisy. "The visual world is noisy. Even well posed visual computations are often numerically unstable, if noise is present in both the scene and ...
متن کاملA High Gain and Forward Body Biastwo-stage Ultra-wideband Low Noise Amplifier with Inductive Feedback in 180 nm CMOS Process
This paper presents a two-stage low-noise ultra-wideband amplifier to obtain high and smooth gain in 180nm CMOS Technology. The proposed structure has two common source stages with inductive feedback. First stage is designed about 3GHz frequency and second stage is designed about 8GHz. In simulation, symmetric inductors of TSMC 0.18um CMOS technology in ADS software is used.Simulations results ...
متن کاملInductive Classification of Semantically Annotated Resources through Reduced Coulomb Energy Networks
The tasks of resource classification and retrieval from knowledge bases in the Semantic Web are the basis for a lot of important applications. In order to overcome the limitations of purely deductive approaches to deal with these tasks, inductive (instance-based) methods have been introduced as efficient and noise-tolerant alternatives. In this paper we propose an original method based on a non...
متن کاملEecient Noise-tolerant Learning from Statistical Queries
In this paper, we study the extension of Valiant's learning model [32] in which the positive or negative classi cation label provided with each random example may be corrupted by random noise. This extension was rst examined in the learning theory literature by Angluin and Laird [1], who formalized the simplest type of white label noise and then sought algorithms tolerating the highest possible...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009