Bhavesh.Amin

Bhavesh.Amin Essay CSC 4810-Artificial Intelligence ASSG# 4 Support Vector MachineSVM is an implementation of Support Vector Machine (SVM). SupportVector Machine was developed by Vapnik. The main futures of the programare the following: for the problem of pattern recognition, for the problemof regression, for the problem of learning a ranking function. Underlyingthe success of SVM are mathematical foundations of statistical learningtheory. Rather than minimizing the training error, SVMs minimizestructural risk which express and upper bound on generalization error. SVM are popular because they usually achieve good error rates and canhandle unusual types of data like text, graphs, and images. SVMs leading idea is to classify the input data separating themwithin a decision threshold lying far from the two classes and scoring alow number of errors. SVMs are used for pattern recognition. Basically,a data set is used to train a particular machine. This machine can learnmore by retraining it with the old data plus the new data. The trainedmachine is as unique as the data that was used to train it and thealgorithm that was used to process the data. Once a machine is trained, itcan be used to predict how closely a new data set matches the trainedmachine. In other words, Support Vector Machines are used for patternrecognition. SVM uses the following equation to trained the VectorMachine: H(x) = sign {wx + b}Wherew = weight vectorb = thresholdThe generalization abilities of SVMs and other classifiers differsignificantly especially when the number of training data is small. Thismeans that if some mechanism to maximize margins of decision boundaries isintroduced to non-SVM type clas sifiers, their performance degradation willbe prevented when the class overlap is scarce or non-existent. In theoriginal SVM, the n-class classification problem is converted into n two-class problems, and in the ith two-class problem we determine the optimaldecision function that separates class i from the remaining classes. Inclassification, if one of the n decision functions classifies an unknowndatum into a definite class, it is classified into that class. In thisformulation, if more than one decision function classifies a datum intodefinite classes, or no decision functions classify the datum into adefinite class, the datum is unclassifiable. To resolve unclassifiable regions for SVMswe discuss four types ofSVMs: one against all SVMs; pairwise SVMs; ECOC (Error Correction OutputCode) SVMs; all at once SVMs; and their variants. Another problem of SVMis slow training. Since SVM are trained by a solving quadratic programmingproblem with number of variables equals to the number of training data,training is slow for a large number of training data. We discuss trainingof Sims by decomposition techniques combined with a steepest ascent method. Support Vector Machine algorithm also plays big role in internetindustry. For example, the Internet is huge, made of billions of documentsthat are growing exponentially every year. However, a problem exists intrying to find a piece of information amongst the billions of growingdocuments. Current search engines scan for key words in the documentprovided by the user in a search query. Some search engines such as Googleeven go as far as to offer page rankings by users who have previouslyvisited the page. This relies on other people ranking the page accordingto their needs. Even though these techniques help millions of users a dayretrieve their information, it is not even close to being an exact science. The problem lies in finding web pages based on your search query thatactually contain the information you are looking for. READ: Homeless: What Has Been Done To Decrease The Probl EssayHere is the figure of SVM algorithm:It is important to understand the mechanism behind the SVM. The SVMimplement the Bayes rule in interesting way. Instead of estimating P(x) itestimates sign P(x)-1/2. This is advantage when our goal is binaryclassification with minimal excepted misclassification rate. However, thisalso means that in some other situation the SVM needs to be modified andshould not be used as is. In conclusion, Support Vector Machine support lots of real worldapplications such as text categorization, hand-written characterrecognition, image classification, bioinformatics, etc. Their firstintroduction in early 1990s lead to a recent explosion of applications anddeepening theoretical analysis that was now established Support VectorMachines along with neural networks as one of standard tools for machinelearning and data mining. There is a big use of Support Vector Machine inMedical Field. Reference:Boser, B., Guyon, I and Vapnik, V.N.(1992). A training algorithm foroptimal margin classifiers. http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf

Uneven distribution of education through the conflict perspective Essay

Uneven distribution of education through the conflict perspective - Essay Example Education has been a crucial issue in several countries nowadays, especially for those with low literacy level. Its importance is seen in its outcomes, such as productivity, literacy, and poverty alleviation. A literate citizenship is a good source of engineers, economists, technologists, scientists, biologists, doctors, teachers, and so on, enough to produce infrastructures and improve the status of science and technology, medicine, and education itself. A low productivity due to lack of all mentioned places a country in a doom of poverty and economic insecurity. Development specialists recognize these outcomes, and provide information on how education may be better in poor countries in order to be richer. There is thus, a strong connection and a dialectical link between education and the economic security of a nation. This connection is seen in the United States, Germany, United Kingdom, and France, which all keep a high record of literacy rate of 99 percent as compared to others t hat keep low literacy rates, such as Arab states (70.3 percent) Several nations are still keeping a low record of literacy level, particularly third world nations, despite the significant importance of education. Female literacy is also found to be lower than their male counterpart in these areas, indicating the pervasion of gender inequality in education and the traditional roles designated to women.... In Ethiopia, a low 24 percent is indicated for their rural areas, while 83 percent for the urban places. It is clear that education has a strong link to economic capacity of the people and nation. Poor children who are able to study experience a large discrepancy of educational opportunities than those from middle class and upper class families. The Comparison of inequality in education may be seen in the following: areas: Teacher allocation, budget allocation, availability of books, and educational facilities and infrastructures. These areas are reflective of social stratification that exists in education. Despite the already high literacy rates, the first world nations like the United States also exhibit a discrepancy in education between poor and rich neighborhoods and schools. Teacher Allocation There is a low quality of teachers in low-income schools in the United States, and at times when there is a short supply of teachers in almost all parts of the country, those who are provided with teachers with the least training and experience are the poorest schools (Hill, 2008). The richest ones get the reverse of this condition. Far less- qualified teachers are consistently provided to students in low-income and minority schools, while children in the wealthier neighborhoods are allocated with the reverse. It reflects the fundamental flaw in the allocation of teachers and funds to schools, whose allotment depends on whether they are poor or rich. Contributory to this scenario is the fact that senior teachers possess total freedom of choice in where to work and most of them choose the most attractive schools and neighborhoods with few difficulties, and demands on teachers are less severe (Hill, 2008). It leaves the new and

Managerial Applications of Technology - Final Case Study

Managerial Applications of Technology - Final - Case Study Example The very first thing to do is to set up a framework for IT acquisition (p. 592). This entails identifying the appropriate IT applications that would be used in the organizational restructure, justifying that the new set of IT applications to be acquired is worthwhile in terms of costs and benefits, and planning the processes of acquisition and implementation of the newly acquired IT applications (pp 592-594). Some of the options available for IT acquisition are through lease of the required IT applications, outright buying or purchase of the application, developing the applications in-house or subcontracting the whole IT process to external managers/consultants. Whichever option chosen must be cost-effective; it must also help in initiating business process redesigning within the organization (pp. 600-606). If properly handled, this organizational transformation would also be instrumental in connecting the organization’s databases and 2 enterprise systems, and providing a smoo th link or integration to the organization’s business partners (p. 606). All the processes highlighted above could only successful happen if there is effective managerial or leadership establishment. This signifies that all the organization’s employees must support and work hard towards the change. 3 Outsourcing/Off-Shoring: Pros and Cons Question 2: You’ve been asked to work with HR to evaluate the potential for outsourcing some IT functions including the potential use of off-shore resources. What are the advantages and disadvantages associated with outsourcing? What can you share with your HR partner regarding the reasons to outsource, factors to be considered, and potential risks? Based on your knowledge of outsourcing/off-shoring what are your thoughts regarding work that would not be good a candidate for outsourcing/off-shoring? What methods will you suggest to measure the value of an outsourcing/off-shoring relationship? Answer 2: Some organizations percei ve IT outsourcing as a way of subsidizing their capital expenses and working towards cost-effectiveness to maximize profits (p. 506). However, there are some important factors that should be strictly considered before outsourcing/off-shoring some functions of any organization’s IT Department. Some commonly touted advantages of outsourcing/off-shoring include that it is cost-saving; it allows business expansion; it creates exposure to better technical expertise; it is flexible and encourage better quality and improvement; and it supports major organizational transformation (pp.506-510). But some of the disadvantages of outsourcing include de-motivation in in-house IT professionals; having negative effect on business strategy; the problem of confidentiality risks; and the fear of sudden collapse of IT provider. The main reason why an organization may seriously consider outsourcing its IT functions is to reduce the overhead capital 4 expenses on its IT requirements. However, it is helpful to consider some factors before going into outsourcing: these include choosing the right external IT Service Provider; estimating the cost of off-shoring; and identifying which applications should not be outsourced based on the importance of their confidentiality to the organization’s future. Even though all the factors outlined in the