B. Naïve Bayesian Classification In machine learning, Naive Bayesian Classification is a family of a simple probabilistic classifier based on the Bayes theorem (or Bayes’s rule) with Naive (Strong) independence assumption between the features. It is one of the most efficient and effective classification algorithms and represents a supervised learning method as well as a statistical method for classification. Naïve Bayesian classifiers assume that the effect of an attribute value on a given class is independent of the values of the other attributes. In other words Naïve Bayesian classifiers assume that there are no dependencies amongst attributes. This assumption is called class conditional independence. It is made to simplify the computations …show more content…
P(X) is the prior probability of X [20]. The naïve Bayesian classifier, or simple Bayesian classifier, works as follows: 1. Let D be a training set of tuples and their associated class labels. As usual, each tuple is represented by an n-dimensional attribute vector, X = (x1, x2... xn), depicting n measurements made on the tuple from n attributes, respectively, A1, A2... An. 2. Suppose that there are m classes, C1, C2... Cm. Given a tuple, X, the classifier will predict that X belongs to the class having the highest posterior probability, conditioned on X. That is, the naïve Bayesian classifier predicts that tuple X belongs to the class Ci if and only if P(Ci |X) > P(Cj |X) for 1 ≤ j ≤ m, j ≠ i. Thus we maximize P(Ci |X). The class Ci for which P(Ci |X) is maximized is called the maximum posteriori hypothesis. By Bayes’ theorem (Equation (6.10)), P(Ci |X) = P(X|Ci)P(Ci) /P(X) . …show more content…
Given data sets with many attributes, it would be extremely computationally expensive to compute P(XjCi). In order to reduce computation in evaluating P(XjCi), the naive assumption of class conditional independence is made. This presumes that the values of the attributes are conditionally independent of one another, given the class label of the tuple (i.e., that there are no dependence relationships among the attributes). Thus, P(X|Ci) = n ∏ k =1 P(xk|Ci) = P(x1|Ci)×P(x2|Ci)ו•• ×P(xn|Ci). We can easily estimate the probabilities P(x1|Ci), P(x2|Ci),..., P(xn|Ci) from the training tuples. Recall that here xk refers to the value of attribute Ak for tuple X. For each attribute, we look at whether the attribute is categorical or continuous-valued. For instance, to compute P(X|Ci), we consider the following (a) If Ak is categorical, then P(xkjCi) is the number of tuples of class Ci in D having the value xk for Ak, divided by jCi,Dj, the number of tuples of class Ci in D (b) If Ak is continuous-valued, then we need to do a bit more work, but the calculation is pretty straightforward. A continuous-valued attribute is typically assumed to have a Gaussian distribution with a mean μ and standard deviation s, defined
The Tell, by Matthew Hertenstein, is about the power of prediction based on observations of brief samples of others’ behavior. Throughout this book, Hertenstein teaches what tells in early life predict autism, how photographs betray others’ personality and aggressive inclinations, how smiling predicts marital stability, how micro-expressions signal deception, how facial structure predicts companies’ profits, and who wins political elections. In the following few pages, there will be many clues on what tells can predict certain things for the future (Hertenstein, ix).
... middle of paper ... ... In Intelligent Data Engineering and Automated Learning–IDEAL 2006 (pp. 1346-1357. Springer Berlin, Heidelberg.
Statistical Induction- is based on statistical information, it predicts something will happen with numerical probability.
For the purpose of this paper I will refine the problem of induction to enumerative cases of induction. I shall explore whether reliabilism is a successful theory of knowledge, and propose that it is a viable solution to the problem of induction proposed by David Hume, but requires ad hoc amendments in attempt to satisfy the New Riddle of Induction put forth by Nelson Goodman.
This story is that, during the mid 21 century, because of the thaw of the iceberg that was floating in the Arctic, thus human created the Artificial Intelligence to help themselves to face the terrible environment easily. David is a robot like them. But he is the only one that is written into the love. As the first robot has love, he became the experimental article to be a kid for a couple who lost their son. As the time goes by, David still can’t join this family, and the couple thinks he can’t rather than their son exactly, so they make a decision to send to the company that created him to destroy him. However, they didn’t want to finally, but David can’t stay with them anymore. David thinks they don’t like him because he is not a real boy, if he can be a real boy, he will hear stories by his mother before he goes to bed, although he never need to sleep. So he still has a dream that one day, he will be a real boy, because he wants to be with his mother. His best friend and guide, Teddy helped him to find his dream and he says he will see him become a real boy. There is only one hope, Blue Fairy can help him to achieve him dream. However, you know, he did find her, but he was freezed with his best hope, Blue Fairy...
Many theories of logic use mathematical terms to show how premises lead to conclusions. The Bayesian confirmation theory relates directly to probability. When applying this theory, a logician must know the probability of a given situation, have a conditional rule, and then he or she must apply the probability when the conditional rule is applied. This theory is used to determine an outcome based on a given condition. The probability of a given situation is x, when y occurs, or the probability is z if it does not occur. If y occurs, then the outcome of the given would be x. For example, if there is a high probability that a storm will occur if a given temperature drops and there is no temperature change, then it will most likely not rain because the temperature did not change (Strevens, 2012). By using observational data such as weather patterns, a person can arrive at a logical prediction or conclusion that will most likely come true based...
Equation 2: Bayes Theorem of Prior Probabilities A1.... ... middle of paper ... ... Cambridge: Cambridge University Press.
[5] J.S. Fulda. Data Mining and Privacy. In R. Spinello and H. Trvani, editors, Readings in CyberEthics, pages 413-417. Jones and Barlett, Sudbury MA, 2001.
Observational learning is a type of learning that is done by observing the actions of others. It describes the process of learning by watching others, retaining what was learned, and
Due to the development of ICT, adaptive learning, which takes into account individual learners’ needs, is changing. Learners’ learning styles are one of the most significant characteristics. They can be categorized according to a number of criteria which are based on cognitive and emotional components of personality. Their combination leads to the countless individual variants of real learning methods which – to a certain degree – can be influenced by the current e-learning resources. When the e-learning resources can react to the learners’ input characteristics or their learning results, they become adaptive e-learning systems (AES) or intelligent AES.
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
Sentiment analysis, also called as opinion mining, is the field of study that analyzes people’s opinions, sentiments, evaluations, appraisals, attitudes and emotion towards entities such as products, services or organizations, individuals, issues, topics and their attributes. Sentiment analysis and opinion mining mainly focuses on opinions which express or imply positive, negative or neutral sentiments. Due to the big diversity and size of social media there is a need of automated and real time opinion extraction and mining. Mining online opinion is a form of sentiment analysis that is treated as a difficult text classification task.
Perhaps the greatest endeavor that owes itself to induction is science. Its claim to be in the pursuit of truth, of empirical knowledge, is entirely dependent on the validity of inductive reasoning. As such, science has developed ways and means to guarantee the validity of its conclusions; this includes randomizing samples, choosing appropriately sized sample groups and the use of statistics to calculate whether something is merely possible or is probable. Each of these methods (and there may be more) needs to be examined.
There are many different types of students. All students have their own way of studying and learning material. A student’s attitude is the most determining factor in how well a student performs academically. Some students are eager to learn and try their best; however, some students could care less about learning. Each year students decide whether they will succeed or fail in school. All students fall into one category or another. Students can be classified into three categories: Overachievers, Average Joes, and Do Not Give a Rips.
Machine learning systems can be categorized according to many different criteria. We will discuss three criteria: Classification on the basis of the underlying learning strategies used, Classification on the basis of the representation of knowledge or skill acquired by the learner and Classification in terms of the application domain of the performance system for which knowledge is acquired.