The Monty Hall problem was first featured on the classic game show "Let's make a Deal". In the final segment of the show, contestants were presented with a choice of three different doors. Behind two of the doors would be a goat, and behind the third would be an extravagant prize such as a car. The contestant begins the game by picking one door. The host, Monty Hall, would then open one of the remaining doors.

Text mining (deriving information from text) is a wide field which has gained popularity with the huge text data being generated. Automation of a number of applications like sentiment analysis, document classification, topic classification, text summarization, machine translation, etc has been done using machine learning models. Spam filtering is a beginner's example of document classification task which involves classifying an email as spam or non-spam (a.k.a. Spam box in your Gmail account is the best example of this. So lets get started in building a spam filter on a publicly available mail corpus.

Machine Learning is a vast area of Computer Science that is concerned with designing algorithms which form good models of the world around us (the data coming from the world around us). Within Machine Learning many tasks are - or can be reformulated as - classification tasks. In classification tasks we are trying to produce a model which can give the correlation between the input data and the class each input belongs to. This model is formed with the feature-values of the input-data. For example, the dataset contains datapoints belonging to the classes Apples, Pears and Oranges and based on the features of the datapoints (weight, color, size etc) we are trying to predict the class. We need some amount of training data to train the Classifier, i.e. form a correct model of the data.

And why is it useful for BigData? 29 What is "continuous features binning"? What is a Standard Scaling? 38 Why are statistical distributions important? What is a Bias - Variance tradeoff? What is a training set, a validation set, a test set and a gold set in supervised and unsupervised learning? What is a cross-validation and what is an overfitting?

An important problem in Bayesian networks classifiers (BNC) is to discover relevant variables that can achieve optimal classification performance. We propose a method based on Bayesian inference for estimating and incorporating feature relevance in classification using BNCs. We empirically validate our method on an application to event recognition in natural images using object and scene information.

Several problems requiere the combination of temporal and spatial reasoning under uncertainty, such as wind prediction for electricity generation in wind farms. In this work we propose a probabilistic spatial-temporal model (PSTM) focused on prediction problems, based on two common properties of these scenarios: sparsity and multivariable mutual information. The proposed spatial-temporal model is essentially a Bayesian network that represents the dependencies between a target variable of interest and a subset of predictor variables in different times and spaces. We developed an algorithm for learning the structure of the model based on a stochastic search of the optimal subset of predictor variables. The proposed model has been applied for wind prediction at different locations in Mexico, using information from several locations at different times. The PSTM is evaluated in terms of predictive accuracy for different time horizons — 1 to 24 hours; and compared to a dynamic Bayesian network (DBN) developed for wind prediction. The performance of the PSTM is in general competitive, and in most cases superior to the DBN.

Dynamic Bayesian Networks (DBNs) bring efficient tools to model complex multivariate dynamical systems learned from collected data and/or expert knowledge. Notwithstanding, the underlying generative Markov model is supposed homogeneous; neither its topology nor its parameters evolve over time. Thus, learning a DBN to model a non-stationary process with this belief will lead to poor prediction capabilities. In order to account for nonstationary processes, we build on a framework to identify transitions between underlying models and a framework to learn them in real time, without making hypothesis about their evolution. We present the tool performances on simulated datasets. Since we aim to use this to model and predict incongruities within an Intrusion Detection System (IDS) in near real-time, great care is ascribed to the capability to correctly detect transition times. Our prior results display the precision of our algorithm in the choice of transitions and therefore the quality of identified networks. At last we suggest future work.

Receiver operating characteristic or ROC curves are of great interest in evaluating many security systems such as biometric authentication. They visualize the trade-off between the number of security breaches and the level of convenience. In the earlier work, ROC curves and their decision boundaries were studied for various classifiers. Here, further studies are conducted to identify problems of ROC curve analysis when artificial neural network (ANN) classifiers' net values are used. Graphical decision boundaries and experimental results on the IRIS biometric authentication system reveal the over-fitting in the ROC curve analysis. This graphical decision boundaries suggest that ANN classifiers with two output units are more desirable than those with a single output unit for two class classification problems.

Many historians believe that the Biblical book of Isaiah was written by two authors approximately two hundred years apart, generally called First Isaiah and Second Isaiah. Some even believe that the second part was itself written by two or more authors. In this paper we use natural language processing techniques to study this hypothesis. We used the Stanford parser to parse the book of Isaiah. Using Student’s t and two measures of text complexity, average sentence length and average tree height, we were able to differentiate the second part of Second Isaiah, commonly called Third Isaiah, from the rest of the book. We then used MALLET’s implementation of LDA to identify ten topics in the book. Using ANOVA, we were able to find two topics that could differentiate selected parts of Isaiah. We then successfully used MALLET's implementation of the Naive Bayes algorithm to find differences between First Isaiah and Second Isaiah and also to differentiate the two parts of Second Isaiah. Finally, we showed that the same technique could be used to easily differentiate Isaiah from another prophetic book of the Bible, I Samuel.

Non-impeding noisy-And Trees (NATs) provide a general, expressive, and efficient causal model for conditional probability tables (CPTs) in discrete Bayesian networks (BNs). A BN CPT may either be directly expressed as a NAT model or be compressed into one. Once CPTs in BNs are so expressed or compressed, complexity of inference (both space and time) can be significantly reduced. The most important operation in encoding or compressing CPTs into NAT models is extracting the NAT structure from interaction patterns between causes. The existing method does so by referencing a NAT database and an associated search tree. Although both are constructed offline, their complexity is exponential on the number of causes. In this work, we propose a novel method for NAT extraction from causal interaction patterns based on bipartition of causes. The method does not require the support of a NAT database and the related search tree, making NAT extraction more efficient and flexible.