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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 13236 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
The use of the area under the ROC curve in the evaluation of machine learning algorithms
 PATTERN RECOGNITION
, 1997
"... In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multilayer Perceptron, kNe ..."
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Cited by 685 (3 self)
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In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multilayer Perceptron, k
Types of Machine Learning Algorithms
"... Machine learning algorithms are organized into taxonomy, based on the desired outcome of the algorithm. Common algorithm types include: • Supervised learning where the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is t ..."
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Machine learning algorithms are organized into taxonomy, based on the desired outcome of the algorithm. Common algorithm types include: • Supervised learning where the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task
Parallelizing Machine Learning Algorithms 1
"... Abstract—Implementing machine learning algorithms involves of performing computationally intensive operations on large data sets. As these data sets grow in size and algorithms grow in complexity, it becomes necessary to spread the work among multiple computers and multiple cores. Qjam is a framewor ..."
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Abstract—Implementing machine learning algorithms involves of performing computationally intensive operations on large data sets. As these data sets grow in size and algorithms grow in complexity, it becomes necessary to spread the work among multiple computers and multiple cores. Qjam is a
Scalability Of Machine Learning Algorithms
, 1993
"... 10 The Author 13 Acknowledgements 15 1 Introduction 16 1.1 Definition of Learning : : : : : : : : : : : : : : : : : : : : : : : : 16 1.2 The objectives of ML : : : : : : : : : : : : : : : : : : : : : : : : : 17 1.3 Approaches taken so far : : : : : : : : : : : : : : : : : : : : : : : 18 1.4 Motivat ..."
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Cited by 5 (1 self)
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10 The Author 13 Acknowledgements 15 1 Introduction 16 1.1 Definition of Learning : : : : : : : : : : : : : : : : : : : : : : : : 16 1.2 The objectives of ML : : : : : : : : : : : : : : : : : : : : : : : : : 17 1.3 Approaches taken so far : : : : : : : : : : : : : : : : : : : : : : : 18 1
Evaluation and Performance Analysis of Machine Learning Algorithms
"... Prediction is widely researched area in data mining domain due to its applications. There are many traditional quantitative forecasting techniques, such as ARIMA, exponential smoothing, etc. which achieved higher success rate in the forecasting but it would be useful to study the performance of alte ..."
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of alternative models such as machine learning methods. This paper gives performance measures of various machine learning algorithms used for prediction. The goal is to find how different machine learning algorithms gives performance when applied to different types of datasets.
Tested Paradigm to Include Optimization in Machine Learning Algorithms
"... Abstract—Optimization is considered to be one of the pillars of statistical learning and also plays a major role in the design and development of intelligent systems such as search engines, recommender systems, and speech and image recognition software. Machine Learning is the study that gives the c ..."
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the computers the ability to learn and also the ability to think without being explicitly programmed. A computer is said to learn from an experience with respect to a specified task and its performance related to that task. The machine learning algorithms are applied to the problems to reduce efforts. Machine
Practical bayesian optimization of machine learning algorithms
, 2012
"... In this section we specify additional details of our Bayesian optimization algorithm which, for brevity, were omitted from the paper. For more detail, the code used in this work is made publicly available at ..."
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Cited by 130 (16 self)
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In this section we specify additional details of our Bayesian optimization algorithm which, for brevity, were omitted from the paper. For more detail, the code used in this work is made publicly available at
Optimizing Sorting with Machine Learning Algorithms
"... The growing complexity of modern processors has made the development of highly efficient code increasingly difficult. Manually developing highly efficient code is usually expensive but necessary due to the limitations of today’s compilers. A promising automatic code generation strategy, implemented ..."
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Cited by 8 (0 self)
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by library generators such as ATLAS, FFTW, and SPIRAL, relies on empirical search to identify, for each target machine, the code characteristics, such as the tile size and instruction schedules, that deliver the best performance. This approach has mainly been applied to scientific codes which can
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