Machine Learning Technology
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves
Machine learning
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If computers could loam from experiencetheirusefulness would be increased. When I write a clumsy program for a contemporary computer a thousand runs on the machine do not re- educate my handiwork. On every execution, each time-wastingblemish and crudity, each
A few useful things to know about machine learning .
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Page 1. A Few Useful Things to Know about Machine Learning Author: Pedro Domingos Department of Computer Science and Engineering University of Washington Published in: Communications of the ACM Volume 55 Issue 10, October 2012 Team of Kiel Gordon Matt
Mllib: Machine learning in apache spark
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Apache Spark is a popular open-source platform for large-scale data processing that is well- suited for iterative machine learning tasks. In this paper we present MLlib, Sparks open- source distributed machine learning library. MLlib provides efficient functionality for a wide
Pattern recognition and machine learning
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Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In
Incremental and decremental support vector machine learning
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An on-line recursive algorithm for training support vector machines, one vector at a time, is presented. Adiabatic increments retain the Kuhn-Tucker conditions on all previously seen training data, in a number of steps each computed analytically. The incremental procedure is
Map-reduce for machine learning on multicore
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We are at the beginning of the multicore era. Computers will have increasingly many cores (processors), but there is still no good programming framework for these architectures, and thus no simple and unified way for machine learning to take advantage of the potential
Correlation-based feature selection for machine learning
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A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach
An introduction to machine learning
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Page 1. An introduction to machine learning Pierre Lison, Language Technology Group (LTG) Department of Informatics HiOA, October 3 2012 Page 2. @ 2012, Pierre Lison INF5820 course Outline Motivation Machine learning approaches My own research Conclusion 2 Page 3
The discipline of machine learning
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Over the past 50 years the study of Machine Learning has grown from the efforts of a handful of computer engineers exploring whether computers could learn to play games, and a field of Statistics that largely ignored computational considerations, to a broad discipline that has
Torch7: A matlab-like environment for machine learning
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Torch7 is a versatile numeric computing framework and machine learning library that extends Lua. Its goal is to provide a flexible environment to design and train learning machines. Flexibility is obtained via Lua, an extremely lightweight scripting language. High
Estimating probabilities: a crucial task in machine learning .
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The evaluation of the Bayesian formula that is used as a basis of many machine learning systems is studied in detail. It is shown that, when used naively (ie assuming the independence of attributes), its classification accuracy heavily depends on the method for
Machine learning from imbalanced data sets 101
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For research to progress most effectively, we first should establish common ground regarding just what is the problem that imbalanced data sets present to machine learning systems. Why and when should imbalanced data sets be problematic When is the problem
Machine learning and data mining
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Over the past decade many organizations have begun to routinely capture huge volumes of historical data describing their operations, their products, and their customers. At the same time, scientists and engineers in many fields find themselves capturing increasingly complex
Gaussian processes for machine learning
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Machine learning usually refers to changes in systems that perform tasks associated with artificial intelligence (AI). Such tasks involve recognition, diagnosis, planning, robot control, prediction, etc.(Nilsson, 1996) Some reasons for adaptation: Some tasks can be hard to
Machine learning and data mining: methods and applications
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Torch: a modular machine learning software library
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Many scientific communities have expressed a growing interest in machine learning algorithms recently, mainly due to the generally good results they provide, compared to traditional statistical or AI approaches. However, these machine learning algorithms are
Representational learning with extreme learning machine for big data
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networks. Index Terms Extreme learning machine, Deep Networks, Representational Learning I. INTRODUCTION THE generalization capability of a machine learning al- gorithm depends upon the features of the dataset. Hence
Introduction to machine learning
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1. Training data: is used to train the classifier-data may be: supervised: the correct classes of the training data are known. unsupervised: the correct classes of the training data are not known reinforcement learning: dont learn a model-directly learn an action! 2. Test data Making decisions based on the input of multiple people or experts has been a common practice in human civilization and serves as the foundation of a democratic society. Over the past few decades, researchers in the computational intelligence and machine learning
mlr: Machine Learning in R
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The mlr package provides a generic, object-oriented, and extensible framework for classification, regression, survival analysis and clustering for the R language. It provides a unified interface to more than 160 basic learners and includes meta-algorithms and model Machine Learning is a subset of AI. With Machine Learning, computers are programmed to learn to do something they are not programmed to do: they learn by discovering patterns and insights from data. In general, we have two types of learning, supervised and unsupervised. While Machine Learning is a subset of AI, we also have subsets within the domain of Machine Learning, including neural networks, natural language processing (NLP), and deep learning. Each of these subsets offers an opportunity for specializing in a career field that will only grow.