A noise tolerant fine tuning algorithm for the Naïve Bayesian learning algorithm
نویسندگان
چکیده
منابع مشابه
Fine tuning the Naïve Bayesian learning algorithm
This work augments the Naïve Bayesian learning algorithm with a second training phase in an attempt to improve its classification accuracy. This is achieved by finding more accurate estimations of the needed probability terms. This approach helps in dealing with the problem of the lack of training data. Unlike many previous approaches that deal with this problem, the proposed method is an eager...
متن کاملthe algorithm for solving the inverse numerical range problem
برد عددی ماتریس مربعی a را با w(a) نشان داده و به این صورت تعریف می کنیم w(a)={x8ax:x ?s1} ، که در آن s1 گوی واحد است. در سال 2009، راسل کاردن مساله برد عددی معکوس را به این صورت مطرح کرده است : برای نقطه z?w(a)، بردار x?s1 را به گونه ای می یابیم که z=x*ax، در این پایان نامه ، الگوریتمی برای حل مساله برد عددی معکوس ارانه می دهیم.
15 صفحه اولIncremental Naïve Bayesian Learning Algorithm based on Classification Contribution Degree
In order to improve the ability of gradual learning on the training set gotten in batches of Naive Bayesian classifier, an incremental Naïve Bayesian learning algorithm is improved with the research on the existing incremental Naïve Bayesian learning algorithms. Aiming at the problems with the existing incremental amending sample selection strategy, the paper introduced the concept of sample Cl...
متن کاملHYDRA: A Noise-tolerant Relational Concept Learning Algorithm
Many learning algorithms form concept descriptions composed of clauses, each of which covers some proportion of the positive training data and a small to zero proportion of the negative training data. This paper presents a method using likelihood ratios attached to clauses to classify test examples. One concept description is learned for each class. Each concept description competes to classify...
متن کاملHYDRA : A Noise - tolerant Relational Concept Learning Algorithm Kamal
Many learning algorithms form concept descriptions composed of clauses, each of which covers some proportion of the positive training data and a small to zero proportion of the negative training data. This paper presents a method using likelihood ratios attached to clauses to classify test examples. One concept description is learned for each class. Each concept description competes to classify...
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: Journal of King Saud University - Computer and Information Sciences
سال: 2014
ISSN: 1319-1578
DOI: 10.1016/j.jksuci.2014.03.008