Add to ORKG90 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2003.Another significant goal of this work was to identify the inductive biases of each algorithm, so that they can be fairlycompared with each other. By examining their biases and properties using the results presented here, it is possible to view 2Pes as a particular generalization of the Winnow algorithm, and IDBD as a further generalization of 2Pes. Understanding these relationships furthers the potential of attribute-efficient algorithms for real-world applications.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD
A new algorithm for on-line learning linear-threshold functions is proposed which efficiently combin...
Linear models in machine learning are extremely computational efficient but they have high represent...
AbstractWe study on-line learning in the linear regression framework. Most of the performance bounds...
90 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2003.Another significant goal of th...
A method of combining learning algorithms is described that preserves attribute efficiency. It yield...
In this dissertation, we consider techniques to improve the performance and applicability of algorit...
In this paper, we present Committee, a new multi-class learning algorithm related to the Winnow fami...
We present a new type of multi-class learning algorithm called a linear-max algorithm. Linearmax alg...
The problem of learning linear discriminant concepts can be solved by various mistake-driven update ...
Invariant synthesis is crucial for program verification and is a challenging task. We present a new ...
The performance of on-line algorithms for learning dichotomies is studied. In on-line learning, the ...
In this paper, we present a new type of multi-class learning algorithm called a linear-max algorithm...
haimCfiz.huji.ac.il The performance of on-line algorithms for learning dichotomies is studied. In on...
A fundamental open problem in computational learning theory is whether there is an attribute e#cien...
Appropriate bias is widely viewed as the key to efficient learning and generalization. I present a n...
A new algorithm for on-line learning linear-threshold functions is proposed which efficiently combin...
Linear models in machine learning are extremely computational efficient but they have high represent...
AbstractWe study on-line learning in the linear regression framework. Most of the performance bounds...
90 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2003.Another significant goal of th...
A method of combining learning algorithms is described that preserves attribute efficiency. It yield...
In this dissertation, we consider techniques to improve the performance and applicability of algorit...
In this paper, we present Committee, a new multi-class learning algorithm related to the Winnow fami...
We present a new type of multi-class learning algorithm called a linear-max algorithm. Linearmax alg...
The problem of learning linear discriminant concepts can be solved by various mistake-driven update ...
Invariant synthesis is crucial for program verification and is a challenging task. We present a new ...
The performance of on-line algorithms for learning dichotomies is studied. In on-line learning, the ...
In this paper, we present a new type of multi-class learning algorithm called a linear-max algorithm...
haimCfiz.huji.ac.il The performance of on-line algorithms for learning dichotomies is studied. In on...
A fundamental open problem in computational learning theory is whether there is an attribute e#cien...
Appropriate bias is widely viewed as the key to efficient learning and generalization. I present a n...
A new algorithm for on-line learning linear-threshold functions is proposed which efficiently combin...
Linear models in machine learning are extremely computational efficient but they have high represent...
AbstractWe study on-line learning in the linear regression framework. Most of the performance bounds...