Machine learning

Extreme learning machine proposed by\cite{elm,elms} is a feed forward neural network classifier with single hidden layer in which the weights between input and hidden layer are initialized randomly. ELM uses analytical approach to compute weights between hidden and output layer\cite{elm} ,which makes it faster compared to other gradient based classifiers. ELM fails to handle class imbalance problem effectively. Many variants of Extreme Learning Machine like Weighted Extreme Learning Machine(WELM)\cite{WELM}

Machine Learning and Predictive Analytics in Healthcare Rupakshi Bhatia Introduction Machine learning has been gaining popularity in healthcare because of its ability to use existing mathematical models and apply them to new instances of an established concept in other data. This ability to automatically identify patterns in data is one of the major reasons for the potential of machine learning in healthcare settings—as well as its ability to fill in the gaps of expert knowledge, adjust for

Introduction Nowadays, data mining and machine learning become rapidly growing topics in both industry and academic areas. Companies, government laborites and top universities are all contributing in knowledge discovery of pattern recognition, text categorization, data clustering, classification prediction and more. In general, data mining is the technique used to analyze data from multi perspectives and reveal the hidden gem behind the enormous amount of data. With the explosive growth of data collections

LITERATURE REVIEW In this chapter, we do an extensive study of malware detection and machine learning. This includes malware types, life cycle of a malware, malware analysis and detection, strategies for malware detection as well as machine learning and its types. MALWARE Malware has been given different names and definitions. The word Malware is used to describe any form of malicious code also called malcode, malicious software or programs. One common definition of malware is the definition by

J. Ross Quinlan. In machine learning community, the decision tree algorithms, Quinlan’s ID3 and its successor C4.5: Programs for machine learning are probably the most popular. The various issues related to decision tree are discussed from the initial state of building a tree to methods of pruning, converting trees into rules and handling other problems such as missing attribute values. Apart from that, Quinlan discusses limitations of programs for machine learning, such as its bias in favour

earnings surprises as well as investigate other factors of return predictability related to size, profitability, and momentum. In addition, my other research interests are in (1) investments, (2) hedge funds and mutual funds, and (3) applications of machine learning in finance. My experience at Villanova, both as a research fellow and a student was formative of my fascination with investments, hedge funds, and mutual funds. My original interest sparked while working with Dr. Velthuis and performing literature

datasets. This report presents the approach and subsequent module that the team was able to accomplish. With an extended effort, this project should be able to incorporate data stream reduction to focus on the Twitter streams of interest and the machine learning techniques that enable for richer detection. 10.2 Introduction 10.2.1 CSpec-DVE The Cyber Spectrum Research & Technology Development Virtual Environment (CSpec-DVE) program enables Reserve Officer Training Corps (ROTC) cadets to contribute technically

‘Machine Learning is a sub discipline of Computer Science that has evolved from Pattern Recognition and Computational Learning Theory.’ ML is akin to Data Mining in the sense that both approaches look for patterns in the data set and while the former trains the program to better its understanding, the latter focuses on extraction of data for human comprehension. A typical application employing ML would involve the design and construction of an algorithm where the program is trained through huge samples

taxonomy. A.THE BASE CLASSIFICATION STEP In this step, the products are classified based on their textual representation. Each product is classified by using a base classifier. The base classifier does not have any aware about the taxonomies. Machine learning techniques such as Naïve Bayes and Logistic Regression are used. The features of the product are extracted from the textual representation of the product. B.THE TAXONOMY AWARE PROCESSING STEP In the taxonomy aware processing step, the result

Corporate Finance in the age of Thinking Computers. Just like humans, computers can now learn and adapt, thanks to machine learning, a subfield in AI. With artificial neural networks to mimic those of the human brain, intelligent computers can learn from examples, meaning that no task specific programming is required. While machine learning technology is still at infant stages in most industries, it is making ground breaking milestones in the financial sector. Among the areas experiencing major