An empirical comparison of supervised learning algorithms

Caruana, Rich; Niculescu-Mizil, Alexandru

doi:10.1145/1143844.1143865

Cited by 1,912 publications

(1,193 citation statements)

References 20 publications

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“…The modular nature of the inference machine architecture allows us to insert any supervised learning classifier as our choice of multi-class predictor g. As the data distribution is highly multi-modal, a high-capacity nonlinear predictor is required. In this work, we use a boosted classifier [28] with random forests for the weak learners, because random forests have been empirically shown to consistently outperform other methods on several datasets [29]. We learn our boosted classifier by optimizing the non-smooth hinge loss [30].…”

Section: Methodsmentioning

confidence: 99%

Pose Machines: Articulated Pose Estimation via Inference Machines

Ramakrishna

Munoz

Hebert

et al. 2014

Computer Vision – ECCV 2014

192

131

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Abstract. State-of-the-art approaches for articulated human pose estimation are rooted in parts-based graphical models. These models are often restricted to tree-structured representations and simple parametric potentials in order to enable tractable inference. However, these simple dependencies fail to capture all the interactions between body parts. While models with more complex interactions can be defined, learning the parameters of these models remains challenging with intractable or approximate inference. In this paper, instead of performing inference on a learned graphical model, we build upon the inference machine framework and present a method for articulated human pose estimation. Our approach incorporates rich spatial interactions among multiple parts and information across parts of different scales. Additionally, the modular framework of our approach enables both ease of implementation without specialized optimization solvers, and efficient inference. We analyze our approach on two challenging datasets with large pose variation and outperform the state-of-the-art on these benchmarks.

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Section: Methodsmentioning

confidence: 99%

Pose Machines: Articulated Pose Estimation via Inference Machines

Ramakrishna

Munoz

Hebert

et al. 2014

Computer Vision – ECCV 2014

192

131

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“…Most elementary undergraduate-level statistics courses teach frequentist inference rather than Bayesian inference [12]. Bayesian method has three parts [2] : Naive Bayes classifier [13][14][15] Case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning.…”

Section: Taxonomy Of Supervised Learning Algorithmsmentioning

confidence: 99%

Section: Taxonomy Of Supervised Learning Algorithmsmentioning

confidence: 99%

The Classification of the Applicable Machine Learning Methods in Robot Manipulators

Hormozi¹,

Hormozi²,

Nohooji³

2012

IJMLC

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Abstract-Supervised machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques used in robotic manipulators. Of course, a single article cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.Index Terms-Machine learning, adaptive control, repetitive control, robot manipulators. I. INTRODUCTIONMachine learning, a branch of artificial intelligence, is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data [1]. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to be able to produce a useful output in new cases. Machine learning, like all subjects in artificial intelligence, require cross-disciplinary proficiency in several areas, such as probability theory, statistics, pattern recognition, cognitive science, data mining, adaptive control, computational neuroscience and theoretical computer science [2]. In this paper we are focused on learning algorithms for robot manipulators. II. MACHINE LEARNING ALGORITHMSMachine learning algorithms are organized into a taxonomy based on the desired outcome of the algorithm.Supervised learning generates a function that maps inputs to desired outputs. For example, in a classification problem, the learner approximates a function mapping a vector into classes by looking at input-output examples of the function for robot manipulators [1].Unsupervised learning models a set of inputs, like clustering [1].Semi-supervised learning combines both labeled and unlabeled examples to generate an appropriate function or classifier in manipulators [3].Reinforcement learning learns how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback in the form of rewards that guides the learning algorithm [1] and [3].Transduction tries to predict new outputs based on training inputs, training outputs, an...

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“…Additionally, there are a large number of other metrics, which are more or less appropriate, for different classes of problems [3]. In fact, real-world applications often have to meet several criteria, which would require the use of customized multi-criteria metrics [18].…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…However, the most common criterion for machine learning and data mining applications is arguably classification performance. This can be illustrated by the fact that one of the largest empirical studies of machine learning metrics focuses entirely on classification performance metrics [3]. The accuracy metric is often used to measure the performance of a classifier in terms of correctness.…”

Section: Introductionmentioning

confidence: 99%

AMORI: A Metric-based One Rule Inducer

Lavesson¹,

Davidsson²

2009

Proceedings of the 2009 SIAM International Conference on Data Mining

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The requirements of real-world data mining problems vary extensively. It is plausible to assume that some of these requirements can be expressed as application-specific performance metrics. An algorithm that is designed to maximize performance given a certain learning metric may not produce the best possible result according to these applicationspecific metrics. We have implemented A Metric-based One Rule Inducer (AMORI), for which it is possible to select the learning metric. We have compared the performance of this algorithm by embedding three different learning metrics (classification accuracy, the F-measure, and the area under the ROC curve), on 19 UCI data sets. In addition, we have compared the results of AMORI with those obtained using an existing rule learning algorithm of similar complexity (One Rule) and a state-of-the-art rule learner (Ripper). The experiments show that a performance gain is achieved, for all included metrics, when using identical metrics for learning and evaluation. We also show that each AMORI/metric combination outperforms One Rule when using identical learning and evaluation metrics. The performance of AMORI is acceptable when compared with Ripper. Overall, the results suggest that metric-based learning is a viable approach.

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An empirical comparison of supervised learning algorithms

Cited by 1,912 publications

References 20 publications

Pose Machines: Articulated Pose Estimation via Inference Machines

Pose Machines: Articulated Pose Estimation via Inference Machines

The Classification of the Applicable Machine Learning Methods in Robot Manipulators

AMORI: A Metric-based One Rule Inducer

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