College supplemental essays: May 2020

Question: Discuss about the Machine Learning. Answer: Introduction Machine Learning (ML) could be termed as a branch of Artificial Intelligence, as they contain the methods, which allows the computers and systems to act more smartly. The result is a unique way of cumulative working of various functions in an orderly manner, rather than simple data insertion and retrieval from applications like database and others, making the machines take better decision are different situations with minimal input from the user. Machine learning is a branch of study, grouped from various different fields, such as computer science, statistics, biology, and psychology. The base functionality of Machine Learning is to identify the best Predictor model for making decisions, by analyzing and learning from previous scenarios, which is the job of a Classifier. The job of Classification is the prediction of unknown scenarios (output) by analyzing known scenarios (input). The process of classification is performed over data set D comprising of the following objects: Let the set size be {X1, X2, |X|} where |X| signifies the attributes count of the set X Class label is Y then the target attribute; ?= y1, y2, |Y|} where Y is the number of classes and Y { 2. Then the basic goal of the machine learning is prediction or classification over the dataset D, such that it relates the attributes in X and classes in Y (Mohri et al, 2012). Classification of Machine Learning is on the basis of the type of input signal or feedback received by the learning system. These are as follows: Supervised learning: System is presented with a set of input condition and an instructor providing the desired output, thus making the system understand what to expect for an answer in a certain scenario, provides the desired outputs (Zhang et al, 2011). Unsupervised learning: There are no markers in the learning methodology, thus stranding the system to discover patterns in the inputs. This type of learning can become a target in itself (unraveling pattern in data) or the end game (feature learning). Reinforcement learning: System is challenged with an unprecedented environment to perform (for instance driving or playing against an opponent). The system is either rewarded or punished, based on the performance it displayed while navigating in the problem sphere. This paper deals with Supervised Learning, the various processes and all the functionalities. Supervised learning This type of learning is based on understanding the mapping of certain attributes and functions in a predefined scenario, then using the knowledge gathered in this process to make a decision based on the mapping learned, in unprecedented scenarios. This way of learning is very important and is a key functionality in multimedia processing (Mohri et al, 2012). In supervised learning a computer model represents a learner system which contains two set of data namely the training data set and the other set is the testing data set. For example consider a system for classification of a particular disease. In such scenario a system will use a data set which contains records of the patients with their diseases. This record is split into training data set and the testing data set. The idea behind this type of learning is to train the learner system with all possible outcomes in the training set, so it can perform with the highest percentage of accuracy in the test set. Which means, the target of a learner is to make out the pattern in the inputs provided in the test set and find a solution from what it has learned in the training set? The classification then can be the categories of diseases for the given example. Similarly the training set might include pictures of dogs like terrier and spaniel, along with the identification of each, now the test set would include another group of unidentified images but of the same set. The target for the learner is to design a rule to guide itself towards a solution in un-known scenarios (Mohri et al, 2012). In supervised learning the training set comprises of various ordered pairs like (a1, b1), (a2, b2), ...,(an, bn), where each ai is represents the set of measurements of a single example data point, and bi is the label for that data point. Consider an example where, a ai might be a group of five attributes for a cricket match, such as run-rate, wickets in hand, strike rate, fielding plan and individual performance. In such case the corresponding bi would be a classification of the game as a win or loose. Generally a testing data is comprises of data but without labels: (an+1, an+2... an+m). As discussed earlier, the target is to make an educated guess in the test set about win or loose by using the learning achieved in training set (Mohri et al, 2012). Supervised learning model and algorithm Following are the steps performed in order to solve a problem of Supervised Learning: Classifying the type of training set. Before proceeding further, an engineer must decide what type of training set he must use for his system. It could be a single unit, a group of it or a bunch of it (Rambhajani et al.,2015). Collect a set. The training set should model the real world entities, so a training set is gathered according. Along with this, possible outcomes are collected to form a set, either through experience or through some empirical measurements (Mohri et al, 2012). Ascertain the input predictive model of the educated function. Learned function's accuracy is directly dependent on the representation of the input. Basically, the input is converted to a feature vector, comprising of various features to model the object. The features should neither be too large in number to confuse the prediction nor should they be too small in number to strangle the decision-making capabilities (Mohri et al, 2012). Ascertain the model of the predictor function and the method employed. The choice may be any model of support vector machine, neural network, decision tree etc. Design finalization. Repetitive use of the carved out method to sharpen the accuracy. Some of the methods may require strengthening of a feature by practicing over a subset or through cross-referencing (Rambhajani et al.,2015). Calculate method's accuracy. Once the accuracy is achieved on the training set, the system must be presented with a test set to ascertain the final accuracy in real world scenario (Mohri et al, 2012). Figure 1: Supervised learning model The base step in supervised learning is handling the dataset. A subject matter expert could be employed to help with feature selection from a given data set. When a subject matter expert is not available then the second best method to ascertain the feature is "Brute Force" method, the use of empirical measurements, judging every possible scenario with all factors in mind and using statistical techniques for arriving at a conclusion, i.e. a feature. This method although is not applicable in all the cases like for a perfect induction, as in all of these cases this feature comprises of significant noise which is to be removed before final induction, thus creating a requirement of over-head pre-processing of the data set. The next step would deal with information preparation and pre-process to be used in Assisted Machine Learning (AML) (Rambhajani et al.,2015). There are various techniques available from several researchers to deal with missing data. Rambhajani et al.(2015), performed a survey and deducted a method to remove noise from the system. Zaremba et al (2016), have also deduced another method for noise removal and is used in several other systems. Konkol (2014), has made a comparative study on six different noise removal techniques by working over base data sets and using a hypothetical test data set (Rambhajani et al.,2015). Issues to be considered in supervised learning The main issues which are to be considered while designing the supervised learning method are as follows: Trade-off between the bias and the variance. Consider a scenario having different set of training data. In such scenario if the learning model tends to be biased to a particular variable y then while training on these data set the model gives incorrect prediction to the exact value for y. Similarly if the model has high variance for a input y then it predicts different output values for that variable for different training sets. Generally the prediction error of any learning model is sum of the bias and the variance of the model. Thus, there exists a tradeoff between the bias and the variance of the model. In case the learning model is too flexible in nature then it will make arrangements within the training data sets differently and thus shall exhibit high variance. The complexity of the function of the classifier and the relative quantity of the training data is the second issue. In case the function of the classifier is a simple function then it tends to be inflexible in nature and this result in the learning model having high bias and low variance values and thus shall be capable to learn from a very small quantity of the training data. On the other hand if the function is complex in nature than it requires high number of data set as the learning model shall be flexible with low bias and high variance making (Marsland, 2015). The dimensionality of the input space also poses challenges to the learning model. In case the input feature vectors comprise of high dimensions than it becomes difficult for the learning model to work upon as high dimensions causes the model to have high variances. Thus the input dimensions need to have low variances and high bias for correct learning model of the classifier (Marsland, 2015). Another issue which arises is the noise coming from the output values. In case the desired output values comprise of error due to human error or errors caused by sensors then the learning model should never try to compute a function that exactly matches the training examples. Doing such execution may lead to over fitting of the function (Marsland, 2015). Algorithm of supervised learning Supervised learning has attracted researchers and thus many types of efficient algorithms have been designed for supervised learning. Many of these algorithms have been used for wide range of applications like for classification of diseases and non-disease attributes, pattern recognition, speech recognition, etc (Ling et al, 2015; Nguyen et al ,2014; Marsland, 2015). Each algorithm of supervised learning has its own set of advantages and disadvantages. Still, there exists no single algorithm that can be used on all supervised learning problem. Some of the popular supervised learning algorithms are as follows: Decision Trees: Decision trees classify the input instances by arranging the instances based on feature values and forms a tree like structure where the tree node represents the feature of the input instance which is to be classified and the branch of the tree signifies a value that can be assigned to the node vector. Classification of the input instances starts at the root node and these instances are arranged based on their assigned feature values (Karimi et al, 2011). Example: Figure 2: Decision tree and data In the figure above there is a decision tree for the data given in the table on the right side. In this example an input instance comprising of at1 with value a1 at2 with a2 etc. can be arranged and classified as a class of Yes or No (Kami?ski et al, 2017). Decision trees are widely used in the classification of input data as they are flexible to be used for wide range of classification problems. Generally, the tree path or the resulting set of the rules of the tree are mutually exclusive which makes each data of the input to be covered in a single rule. This enhances the accuracy of the predictive systems and makes them more scalable (Kami?ski et al, 2017). Nave Bayes Classifier: Nave Bayes classifier comes under the group of Bayesian network based classifier which is generally statistical learning algorithms (Huang et al, 2014). The Nave Bayes Classifier networks comprise of directed acyclic graphs where there is one parent which represents the un observed node and many children nodes representing the observed nodes. There is a sting assumption of independence among the children nodes (Archana et al, 2014). The Nave Bayes makes use of the Bayes theorem to calculate the probability by counting the value frequencies and the combination of values of the historical data (Cohen et al, 2014). The posterior probability is calculated using the Bayes formula as given below: Where, P (c|x) is the posterior probability of class (target) given predictor (attribute). P(c) is the prior probability of class. P (x|c) is the likelihood which is the probability of predictor given class. P(x) is the prior probability of predictor For example, the frequency table is given below with the likelihood table. Figure 3: Frequency table and likelihood table The posterior probability now can be calculated using the above equation and the class with the highest probability becomes the result of the prediction. The advantage of this method is that it requires a very small amount of input training data and is fast to predict the outcome. But the biggest challenge while using this method is that getting an independent set of predictors is not possible always in a real life situation (Ghahramani et al, 2015). K-Nearest Neighbor This method of the supervised learning algorithm is used for classification when there is no prior or very less information about the distribution of the input data. Thus, this method is a good choice where it is required to perform the discriminate analysis when the probability densities are unknown. The classification in this method is done on the majority of the k-nearest neighbor category (Hamid et al., 2010). The input is classified as a new object based on the training samples and the attributes. The classification uses majority vote for the classification of the k objects. For example, consider the given sample data: X1 X2 Result 7 7 NO 7 4 NO 3 4 Yes 1 4 Yes Table 1: sample table The values of the variables x1 and x2 drive the outcome for the data as Yes or No. Now suppose the input value of x1 and x2 are 4 and 7 respectively, then there is no need to perform a lengthy survey in such scenarios this method can be used and the nearest k value shall be the result of the given input and thus the outcome shall be yes (Michalski et al, 2013). Support Vector Machine Support Vector Machine model represents the examples as a point in a given space and these examples than are separated into classes by a clear gap or the hyper plane. The new instances of the examples are then mapped into the same input space and their classes are predicted such that they fall on the either side of the hyper plane (Karamizadeh et al, 2014). Thus, the hyper plane is used for classification and regression. Thus, Support Vector Machine is a linear binary classifier which first classifies the entire input into two categories divided by a wide gap of the hyper plane based on some function criteria. SVM has been widely used for classification as they are effective in high dimensional spaces, is memory efficient and versatile in nature for holding different kernel functions which can be used for decision making. The only challenge this method poses is the refinement to be done for multi variable classification which requires linear binary classifier to be executed recursive ly thereby consuming more time (Liu et al, 2012). Artificial neural network and Deep Learning: Supervised learning can be efficiently implemented using the artificial neural network algorithms like back propagation algorithm. Neural Networks are machine model of human neurons and have a set of weighted inputs. The neuron produces an output based on the threshold function. In the training phase, various combinations of input and weights are used to train the network to give an output. The error from the output obtained is used for learning via back propagation and thus the network gets trained from the error (LeCun et al, 2015).Deep artificial neural networks are a variant of neural networks which have become popular in pattern recognition and machine learning. In the deep Learning model, neural networks with many layers are trained in a layer wise manner. Each layer by learning enhances the quality of learning and the accuracy of the output and henceforth this is used in many applications like computer vision, speech recognition, handwriting recognition, natural language proce ssing etc (Ruslan et al. , 2012; Dalessandro, 2013). Today Deep learning has been implemented in various to products of the company like Microsoft, Google, and Apple for data analysis and natural language processing. Google Voice search and Apple Siri make use of Deep learning method for natural language processing and thus are able to recognize the spoken text (Vincent et al,2010). These applications learn from the user usage every time and thus enhance their quality over the time of their usage (Dahl et al. , 2012). Applications of supervised learning Supervised learning has been used for various applications. There are many algorithms for supervised learning and many of them have proven to be useful in various applications. Any researchers have successfully implemented applications which make use of supervised learning for prediction, for classification for recognition etc. The main popularity of the usage of the supervised learning algorithms is due to its simplicity and that it helps in the development of the application with the available input sets and expected output sets. This makes the automation process to be an easier one. Supervised learning has been used for pattern recognition, speech recognition, handwriting recognition, optical character recognition, image classification, data mining, knowledge mining, data and text classification, spam filtering, single filtering, intrusion detection system, automated systems for traffic lights, flight scheduling, congestion control systems, disease classification and prediction sy stems etc. Some of the notable works are given below Disease classification prediction Supervised learning can be used for diagnosis of any disease where the existing disease patterns and symptoms of the previously known patients are given as the training inputs. After the training is done the new set of inputs for prediction are supplied to the model and the model predicts the probability of the disease i.e. it is present or not (Jordan et al, 2015). It classifies the diseases in the given class of present or absent. Such work has been done for predicting diseases like Breast cancer, Diabetes, heart diseases etc (Yugowati et al ,2013). In a research by Vembandasamy et al. (2015), the authors used Nave Bayes supervised learning algorithm to predict the heart disease of the patients. The data set comprised of 500 patients and the classification of the data was done using 70 % rules. The system accuracy was 87%. Similarly, Iyer et al (2015), performed the prediction of diabetes diseases using decision tree and Nave Bayes classifier. Pattern Recognition Supervised learning algorithms can be used to classify different patterns of images, shapes, handwriting characters, etc. The model is trained with the existing set of input and output patterns after the training the model is used to classify the other trusted input pattern into the various classes identified in the training phase. Pattern recognition abilities have helped in the development of computer vision applications using supervised learning (Murty et al, 2011). Many applications have been devised and been successfully implemented which work on the supervised learning model for image processing and pattern recognition system. For example automated smartcard recharge system is able to recognize the dollars through pattern recognition of the dollars submitted to the machine. Many researchers have used algorithms like Neural networks, Nave Bayes, SVM, K-Means to classify and identify patterns of handwriting, speech images, shapes etc (Smith et al, 2011; Sharma et al, 2015). Intrusion detection: The signature of the intrusions and their consequences are given as the training input to a supervised model. Based on the learning the model then classifies the new set of inputs into intrusion category or safe category. In this way, the supervised learning helps in instruction detection applications. The supervised learning methods are also helping in detecting signatures of cyber-attacks and viruses for the networks. Many types of research have been carried out in this area using various algorithms like neural networks, SVM etc (Poojitha et al, 2010). Conclusion One of the main objectives of the machine learning process is to impart computer an ability to learn from the data or the past experiences and thereby help in solving problems of classification and prediction. Machine learning can be done in 3 different ways i.e. supervised learning, unsupervised learning and reinforcement learning. The main aim of the supervised learning method is to build a model based on the trained input classes and their predicted features. The result is a classifier which assigns a label to the tested class instances with eh class labels formed in the training phase. Supervised learning has proven to be one of the efficient and easier methods of machines learning and hence it is widely used in various applications like speech recognition, pattern recognition, computer vision, intrusion detection, medical diagnosis systems etc. Supervised learning algorithms are more powerful than the other machine learning model like the unsupervised method because in the super vised learning the training data availability provides clear criteria for the optimization of the model. References Alpaydin, E., (2014). Introduction to machine learning. MIT press. Alsheikh, M.A., Lin, S., Niyato, D. and Tan, H.P., (2014). Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Communications Surveys Tutorials, 16(4), pp.1996-2018. Archana, S. and DR Elangovan, K. (2014) Survey of Classification Techniques in Data Mining. International Journal of Computer Science and Mobile Applications, 2, 65-71 Cohen, P.R., and Feigenbaum, E.A. eds., (2014). The handbook of artificial intelligence (Vol. 3). Butterworth-Heinemann. Dahl, G.; Yu, D.; Deng, L.; Acero, A. (2012). "Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition". IEEE Transactions on Audio, Speech, and Language Processing. 20 (1): 3042. Dalessandro, B., (2013). Bring the noise: Embracing randomness is the key to scaling up machine learning algorithms. Big Data, 1(2), pp.110-112. Deng, H.; Runger, G.; Tuv, E. (2011). The bias of important measures for multivalued attributes and solutions. Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN). Ghahramani, Z., (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), p.452. Hamid, P, Alizadeh, H., and Minati, B. (2010). A Modification on K-Nearest Neighbor Classifier, Global Journal of Computer Science and Technology, Vol. 10, No. 14 (Ver.1.0), pp. 37-41. Huang, G., Huang, G.B., Song, S. and You, K., (2015). Trends in extreme learning machines: A review. Neural Networks, 61, pp.32-48. Huang, G., Song, S., Gupta, J.N. and Wu, C., (2014). Semi-supervised and unsupervised extreme learning machines. IEEE transactions on cybernetics, 44(12), pp.2405-2417. Iyer, A., Jeyalatha, S. and Sumbaly, R. (2015) Diagnosis of Diabetes Using Classification Mining Techniques. International Journal of Data Mining Knowledge Management Process (IJDKP), 5, 1-14. Jordan, M.I., and Mitchell, T.M., (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), pp.255-260. Kami?ski, B.; Jakubczyk, M.; Szufel, P. (2017). "A framework for sensitivity analysis of decision trees". Central European Journal of Operations Research. doi:10.1007/s10100-017-0479-6 Karamizadeh, S., Abdullah, S.M., Halimi, M., Shayan, J. and Rajabi, M.J. (2014) Advantage and Drawback of Support Vector Machine Functionality. 2014 IEEE International Conference on Computer, Communication and Control Technology (I4CT), Langkawi, 2-4 September 2014, 64-65 Karimi, K. and Hamilton, H.J. (2011). "Generation and Interpretation of Temporal Decision Rules", International Journal of Computer Information Systems and Industrial Management Applications, Volume 3. Konkol, M., (2014),. Brainy: A machine learning library. In International Conference on Artificial Intelligence and Soft Computing (pp. 490-499). Springer, Cham. LeCun, Y., Bengio, Y. and Hinton, G., (2015). Deep learning. Nature, 521(7553), pp.436-444. Ling, J. and Templeton, J., (2015). Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty. Physics of Fluids, 27(8), p.085103. Liu, X. Y., Gao, C. H.and P. Li, (2012). A comparative analysis of support vector machines and extreme learning machines, Neural Network., vol. 33, pp. 5866. Marsland, S., (2015). Machine learning: an algorithmic perspective. CRC press. Michalski, R.S., Carbonell, J.G. and Mitchell, T.M. eds., (2013). Machine learning: An artificial intelligence approach. Springer Science Business Media. Mohri, M, Rostamizadeh, A., Talwalkar, A. (2012). Foundations of Machine Learning. USA, Massachusetts: MIT Press. ISBN 9780262018258. Murty, N.M.; Susheela Devi, V. (2011). Pattern Recognition: An Algorithmic Approach. ISBN 0857294946. Nguyen, D.H., and Patrick, J.D.,( 2014). Supervised machine learning and active learning in the classification of radiology reports. Journal of the American Medical Informatics Association, 21(5), pp.893-901. Poojitha, G. Kumar, K. N., and Reddy, P.J. (2010). Intrusion Detection Using Artificial Neural Network, International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1-7. Rambhajani, M., Deepanker, W. and Pathak, N. (2015). A Survey on Implementation of Machine Learning Techniques for Dermatology Diseases Classification. International Journal of Advances in Engineering Technology, 8, 194-195. Ruslan, S, Joshua, T., (2012). "Learning with Hierarchical-Deep Models". IEEE Transactions on Pattern Analysis and Machine Intelligence. 35: 195871. doi:10.1109/TPAMI.2012.269 Sharma, P. and Kaur, M. (2013) Classification in Pattern Recognition: A Review. International Journal of Advanced Research in Computer Science and Software Engineering, 3, 298. Singh, Y., Bhatia, P.K., and Sangwan, O. (2007). A Review of Studies on Machine Learning Techniques. International Journal of Computer Science and Security, 1, 70-84. Smith, M.R, and Martinez, T. (2011). "Improving Classification Accuracy by Identifying and Removing Instances that Should Be Misclassified". Proceedings of International Joint Conference on Neural Networks (IJCNN 2011). pp. 26902697. Vembandasamy, K., Sasipriya, R. and Deepa, E. (2015). Heart Diseases Detection Using Naive Bayes Algorithm. IJISET-International Journal of Innovative Science, Engineering Technology, 2, 441-444. Vincent, P, Larochelle, H, Lajoie, I, Bengio, Y, Manzagol, P.(2010). "Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion". The Journal of Machine Learning Research. 11: 33713408. Wenger, E., (2014). Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge. Morgan Kaufmann. Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J., (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann. Xia, R., Pan, Y., Lai, H., Liu, C. and Yan, S., (2014). Supervised Hashing for Image Retrieval via Image Representation Learning. In AAAI (Vol. 1, pp. 2156-2162). Yugowati P, Shaou-Gang, M. Wee,H. 92013). Supervised learning approaches and feature selection - a case study in diabetes, International Journal of Data Analysis Techniques and Strategies 2013 - Vol. 5, No.3 pp. 323 337 Zaremba, W., Mikolov, T., Joulin, A. and Fergus, R., (2016). Learning simple algorithms from examples. In International Conference on Machine Learning (pp. 421-429). Zhang, J, Zhan, Z., Lin, Y., Chen, N, Gong, Y; Jing-hui, Z. Chung, H, Li, Yun; Shi, Yu-hui (2011). "Evolutionary Computation Meets Machine Learning: A Survey" (PDF). Computational Intelligence Magazine. IEEE. 6 (4): 6875.

College supplemental essays

Wednesday, May 6, 2020

Analysis Of The Movie I re Your Problem Annie

Supervised Learning Model and Algorithm-Free-Samples for Students