My final project was to implement and compare a number of Naive Bayes and boosting algorithms. For this task I chose to implement two Naive Bayes algorithms that are able to make use of binary attributes, the multivariate Naive Bayes and the multinomial Naive Bayes with binary attributes. For the boosting side of the algorithms I chose to implement AdaBoost, and its close bother AdaBoost*. Both...

In this paper investigation of the performance criterion of a machine learning tool, Naive Bayes Classifier with a new weighted approach in classifying breast cancer is done . Naive Bayes is one of the most effective classification algorithms. In many decision making system, ranking performance is an interesting and desirable concept than just classification. So to extend traditional Naive Baye...

Despite its simplicity, the naive Bayes learning scheme performs well on most classiication tasks, and is often signiicantly more accurate than more sophisticated methods. Although the probability estimates that it produces can be inaccurate, it often assigns maximum probability to the correct class. This suggests that its good performance might be restricted to situations where the output is c...

The naive Bayesian classiier provides a very simple yet surprisingly accurate technique for machine learning. Some researchers have examined extensions to the naive Bayesian classiier that seek to further improve the accuracy. For example, a naive Bayesian tree approach generates a decision tree with one naive Bayesian classiier at each leaf. Another example is a constructive Bayesian classiier...

Psychology is the luckiest of the sciences because it owns the most interesting questions, the foremost being, "Why do people do what they do?" Naively, one might expect that research addressing this question would focus on the most important behaviors, but instead most studies choose behavioral dependent variables on the basis of their procedural feasibility and suitability for theory testing....

Naive Bayes is a well-known and studied algorithm both in statistics and machine learning. Bayesian learning algorithms represent each concept with a single probabilistic summary. This paper presents a variant of the Naive Bayes method, in which the original training set is augmented in the following fashion: Leave-One-Out procedure is applied over the training set, and incorrectly classified i...

Naive Bayesian classiiers utilise a simple mathematical model for induction. While it is known that the assumptions on which this model is based are frequently violated, the predictive accuracy obtained in discriminate classiication tasks is surprisingly competitive in comparison to more complex induction techniques. Adjusted probability naive Bayesian induction adds a simple extension to the n...

A source of much difficulty and confusion in the interpretation of quantum mechanics is a “naive realism about operators.” By this we refer to various ways of taking too seriously the notion of operator-as-observable, and in particular to the all too casual talk about “measuring operators” that occurs when the subject is quantum mechanics. Without a specification of what should be meant by “mea...

The most common model of machine learning algorithms involves two life-stages, namely the learning stage and the application stage. The cost of human expertise makes difficult the labeling of large sets of data for the training of machine learning algorithms. In this paper, we propose to challenge this strict dichotomy in the life cycle while addressing the issue of labeling of data. We discuss...

We introduce naive traders in bilateral trading. These traders report their true types in direct mechanisms and bid/ask their values/costs in auctions. We show that by expropriating naive traders in direct mechanisms, the mechanism designer can subsidize additional trades by strategic traders and improve efficiency ex-post compared to when both traders are surely strategic. In fact, complete ex...