a. Support Vector Machine(SVM): Over the past several years, there has been a significant amount of research on support vector machines and today support vector machine applications are becoming more common in text classification. In essence, support vector machines define hyperplanes, which try to separate the values of a given target field. The hyperplanes are defined using kernel functions. The most popular kernel types are supported: linear, polynomial, radial basis and sigmoid. Support Vector Machines can be used for both, classification and regression. Several characteristics have been observed in vector space based methods for text classification [15,16], including the high dimensionality of the input space, sparsity of document vectors, linear separability in most text classification problems, and the belief that few features are relevant.
Assume that training data with for are given. The dual formulation of soft margin support vector machines (SVMs) with a kernel function K and control parameter C is
(1)
s.t. , ,
The kernel function
where <,> denotes an inner product between two vectors, is introduced to handle nonlinearly separable cases without any explicit knowledge of the feature mapping . The formulation (1) shows that the computational complexity of SVM training depends on the number of training data samples which is denoted as n. The computational complexity of training depends on the dimension of the input space. This becomes clear when we consider some typical kernel functions such as the linear kernel, ,
The polynomial kernel, ,
The Gaussian RBF (Radial Base Function) kernel, ,
Where d is the degree of polynomial and γ is a parameter to control. The evaluation...
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...of the total number of correct predictions. It is calculated by the following formula
True positive (TP) is the proportion of positive cases which are correctly classified and calculated by the following formula
False positive (FP) is the proportion of negative cases that are incorrectly classified as positive and calculated by the following formula
True negative (TN) is the proportion of negative cases that are classified correctly and calculated by the following formula
False negative (FN) is the proportion of positive cases that are incorrectly classified as negative and calculated by the following formula
Precision (P) is the proportion of the predicted positive cases that are correct and calculated by using the formula
Accuracy is the proportion of the total number of correct predictions. It is calculated by the following formula
Discussion: The percent of errors is 59.62%. Several errors could have happened during the experiment. Weak techniques may occur.
The first equation is the paternity index (PI), also known as the system index (SI). It is a ratio of the likelihood that an allele is passed down from the supposed father compared to an allele being passed down from a random man. Harmening (2005) states, in the equation for paternity index (X/Y), X is the chance t...
Test of the “harmless error” rule. Law and Human Behavior Vol. 21, No. 1, p.
closer the line of best fit is to 1; the more evidence there is to
To make sure it is a fair test; the procedure is repeated a couple of
Diagnostic decision making in medicine involves a cognitive process. As a clinician/physician, their main task is to make sure to give reasoned decisions about their patients based on the available information. They start with internalizing sets of data and observation gathered from the patient, and then producing decisions and series of options. Most data are obtained from the history and physical examinations of the patients, usually these are efficients for making a diagnosis, but often, more information is required. In some cases, an urgent decision must be made, these processes need to fit sets of observations into a specific category (Al-Sayyari, 2007). Myers (1962) developed type indicator personality measure based on Jung (1923) theory
...rities the computer generates the an assessment of the level of risk. Since no two cases or alike, it is unlikely that what worked for one family will work for all families. Each case is unique and requires the skill and judgement only a human being can provide.
The Myers-Briggs Type Indicator Test, otherwise known as the MBTI test, is a questionnaire intended to measure and evaluates the psychological preferences of individuals in relation to their perception of the world, and generally their decision making ability. This was developed and got form typological theories that were deduced by Cal Gustav Jung. He categorized them into four psychological functions, which each unique individual uses to experience the world. They include feeling, sensation, intuition and thinking (Myers I. B., 1987).
The first test completed was the Myer Briggs Type Indicator (MBTI) test. The results of these test gave me a raw score, revealing that i am a "INFJ" type, which in layman terms means that I am a 66% an introvert, 9% intuitive, 28% feeling and 3% judging (Humanmetrics.com, 2015). This test takes to classifying people into different
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...
Before learning the methods from the computer tutorial, I was confused about certain test. B...
When test results don’t have accuracy, additional testing may be needed to authenticate the results.
The Myers–Briggs Type Indicator (MBTI) is the most popular test of personality which has been used in thousands of organizations worldwide. It is designed to indicate psychological preferences in which human perceive the world and take a decision. This test consists of four principal psychological functions, which each consisting of two opposite preferences as can be seen in figure 1. The main idea is from 16 types of personality, only one that each person can be matched.
My thesis contain the identification of accurately classifying the sentiment in text from micro blogs. This addresses the problem by retrieving opinions, performing processing on the data and analyzing the data using machine learning techniques to classify them by sentiment as positive, negative or neutral. I proposed sentimental natural language processing method for processing the text and use various machine learning algorithms and feature selection methods to determine the best approach. The approaches towards sentiment analysis are machine learning based methods, lexicon based methods and linguistic analysis. I proposed sentimental natural language processing Model for processing text to remove irrelevant features that do not affect its orientation. Sentimental natural language processing model carries opinions in natural language process as well as unstructured reviews with pointers, punctuations, emotions, repeated words, symbols, WH questions, URL’s are preprocessed to extract relevant features while sanitizing inputs. Sentimental natural language processing measures the importance of feature...
The judge exhibited a strong mathematical fallacy when he assumed that repeating the test could not tell us anything about the reliability of the first results. What he didn’t realize was that by doing a test twice and obtaining the same result, it would tell us something about the possible accuracy of the original result.