What is Machine Learning and how it works? Machines leaning is basically a method of teaching computers to make predictions based on historical data. The computers then improve its internal programs using this data. To illustrate this let us consider the example of a normal email filter which automatically filters out spam emails from an inbox, this is possible as the email engine is programmed to learn to distinguish spam and non-spam messages. Over time as the program keeps on learning its performance
because of the size of dataset. So it is very important to make study on data analysis. Most of the technologies are blended with data mining or we can say that data mining is vital and indispensible concept for every technology. Traditional machine learning algorithms like decision trees or artificial neural networks are examples of embedded approaches [9][10].Data mining tasks are mainly divided into predictive and descriptive. Predictive refers to predict the particular attribute based on other
Dynamic News Classification using Machine Learning Introduction Why this classification is needed ? (Ashutosh) The exponential growth of the data may lead us to a time in future where huge amount of data would not be able to be managed easily. Text Classification is done through Text Mining study which would help sorting the important texts from the content or a document to manage the data or information easily. //Give a scenario, where classification would be mandatory. Advantages of classification
Building blocks of Machine learning: Mahout and Spark Machine learning is the new boom in software industry which helps in training the computer to think, organize and process data by itself. The main intent of machine learning is that machine learns to observe data, extract important information from it and grasp on its own to predict, recommend or alter any action without any human mediation. This requires various algorithms over varied systems. For the ease of these algorithms, Apache has come
Real valued classification is a popular decision making problem, having wide practical application in various fields. Extreme Learning Machine (ELM) pro- posed by Huang et al.[1], is an effective machine learning technique for real valued classification. ELMis a single hidden layer feedfo5 rward network in which the weights between input and hidden layer are initialized randomly. ELM uses analytical approach to compute weights between hidden and output layer [2], which makes it faster compared
Extreme learning Machine (ELM) [1] is a single hidden layer feed forward network (SLFN) introduced by G. B. Huang in 2006. In ELM, the weights between input and hidden neurons and the bias for each hidden neuron are assigned randomly. The weight between output neurons and hidden neurons are generated using the Moore Penrose Generalized Inverse [18]. This makes ELM a fast learning classifier. It surmounts various traditional gradient based learning algorithms [1] such as Back Propagation (BP) and
When popularity of machine learning models increased, a number of automated trading systems were build around these models. But rst, let 's take a look at the history of machine learning models in the eld of nancial predictions. At rst, White (1988) applied articial neural networks (ANN) to reveal nonlinear regularities in the IBM stock price movements. Subsequently, Kamijo and Tanigawa (1990) used a recurrent neural network for the recognition of price patterns in the Japanese market. Cheng
Parallel Support Vector Machine Junfeng Wu Junming Chen May 6, 2016 1 INTRODUCTION Support vector machines is a supervised machine learning alogrithom used for classification. The problem could be written : minimize 1 |w |2 2 yi((w,xi)+b)−1≥0 where w is a linear combination of the training data: n w = αi k(xi ) i=1 this could be further written in a dual form[5]: min 1αTQα−eTα α2 0≤αi ≤C, yTα=0, ∀i ≤n where Q is the kernel matrix. This dual form is a quadratic programming problem with linear
INTRODUCTION Deep Learning (or deep structured learning, or hierarchical learning or deep machine learning) is a branch of Machine Learning which is based on a set of algorithms that attempts to model high level abstractions in data by using a deep graph with multiple processing layers which are composed of multiple non-linear and linear transformations. Applying Deep Learning to Building Automation Sensors Sensors such as motion detectors, photocells, CO2 and smoke detectors are used primarily for
My current research interests include image/video analysis and processing, computer vision, pattern recognition, and machine learning. I have publications in several journals and conference proceedings, including the highly ranked IEEE TPAMI journal, Pattern Recognition Journal, IEEE ICIP conference, ICPR conference, and IEEE ICTAI conference. I had joint research work with other professors through funded research projects, graduate students co-supervising, and mutual cooperative research efforts