Instance-based prediction of real-valued attributes
نویسندگان
چکیده
منابع مشابه
Instance-Based Prototypical Learning of Set Valued Attributes
The aim of this project is to investigate and develop new machine learning techniques which can be applied to agent based applications such as those that assist in information filtering. The motivation for this work emerged from reviewing the literature on Interface Agents, and applying existing machine learning techniques to an intelligent interface agent which filtered incoming electronic mai...
متن کاملDiscretization of Continuous-valued Attributes and Instance-based Learning
Recent work on discretization of continuous-valued attributes in learning decision trees has produced some positive results. This paper adopts the idea of discretization of continuous-valued attributes and applies it to instance-based learning (Aha, 1990; Aha, Kibler & Albert, 1991). Our experiments have shown that instance-based learning (IBL) usually performs well in continuous-valued attribu...
متن کاملMultiple-Instance Learning of Real-Valued Data
The multiple-instance learning model has received much attention recently with a primary application area being that of drug activity prediction. Most prior work on multiple-instance learning has been for concept learning, yet for drug activity prediction, the label is a real-valued affinity measurement giving the binding strength. We present extensions of k-nearest neighbors (k-NN), Citation-k...
متن کاملReal-Valued Multiple-Instance Learning with Queries
While there has been a significant amount of theoretical and empirical research on the multiple-instance learning model, most of this research is for concept learning. However, for the important application area of drug discovery, a real-valued classification is preferable. In this paper we initiate a theoretical study of real-valued multiple-instance learning. We prove that the problem of find...
متن کاملA Bayesian Discretizer for Real-Valued Attributes
Discretization of real-valued attributes into nominal intervals has been an important area for symbolic induction systems because many real world classiication tasks involve both symbolic and numerical attributes. Among various supervised and unsupervised discretization methods, the information gain based methods have been widely used and cited. This paper designs a new discretization method, c...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Intelligence
سال: 1989
ISSN: 0824-7935,1467-8640
DOI: 10.1111/j.1467-8640.1989.tb00315.x