# machine learning algorithm

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There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.

** Practical bayesian optimization of machine learning algorithms **

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The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a black art requiring expert experience, rules of thumb, or sometimes brute-force search. There is

** Genetic algorithms and machine learning **

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There is no a priori reason why machine learning must borrow from nature. A field could exist, complete with well-defined algorithms data structures, and theories of learning without once referring to organisms, cognitive or genetic structures, and psychological orThis paper introduces the current machine learning approach to solving spatial modeling problems in the domain of landslide susceptibility assessment. The latter is introduced as a classification problem, having multiple (geological, morphological, environmental etc.)

** A supervised machine learning algorithm for arrhythmia analysis**

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A new machine learning algorithm for the diagnosis of cardiac arrhythmia from standard 12 lead ECG recordings is presented. The algorithm is called VF15 for Voting Feature Intervals. VF15 is a supervised and inductive learning algorithm for inducing classification knowledgeto minimize an error function over the training data, and decision tree algorithms employ a problem is absent), it still be very difficult computationally for the learning algorithm to find In most applications of machine lear- ning, the true function f cannot be represented by any [HTML]

** Machine learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis**

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Background: With appropriate algorithms computers can learn to detect patterns and associations in large data sets. The authors goal was to apply machine learning to arterial pressure waveforms and create an algorithm to predict hypotension. The algorithm detects

** The use of the area under the ROC curve in the evaluation of machine learning algorithms **

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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. Multiscale Classifier, Perceptron

** How the machine thinks: Understanding opacity in machine learning algorithms **

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This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan

** Big data and machine learning in health care**

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Although we are reluctant to repeat the clich , even with the best machine learning algorithms the maxim of garbage in, garbage out remains true. Whether an algorithm is high or low on the machine learning spectrum, best analytic practices must be used to ensure that the

** Dlib-ml: A machine learning toolkit**

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It can be used to easily kernelize any algorithm that requires only the ability to perform vector addition, subtraction, scalar multiplication, and inner products. The kcentroid object enables the library to provide a number of useful kernel-based machine learning algorithms

** Scikit-learn: Machine learning in Python**

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scikit-learns k-means algorithm is implemented in pure Python. Its performance is lim- ited by the fact that numpys array operations take multiple passes over data. 6. Conclusion Scikit-learn exposes a wide variety of machine learning algorithms both supervised and unsuper

** Machine learning in action**

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tasks Everyday data analysis Implementing classic algorithms like Apriori Adaboos===== Table of ContentsPART 1 CLASSIFICATION Machine learning basics Classifying Improving classification with the AdaBoost meta algorithm PART 2

** Large-scale machine learning with stochastic gradient descent**

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envisions very large datasets. This contribution ad- vocates stochastic gradient algorithms for large scale machine learning prob- lems. The first section describes the stochastic gradient algorithm . The sec- ond section presents

** Map-reduce for machine learning on multicore**

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Our work is in distinct contrast to the tradition in machine learning of designing (often ingenious) ways to speed up a single algorithm at a time. Specifically, we show that algorithms that fit the Statistical Query model [15] can be written in a certain summation form, which allows

** Niche modeling perspective on geographic range predictions in the marine environment using a machine learning algorithm **

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Materials and Methods Eighteen marine fish species occurring in FAO Fishing Area 31 were selected for analysis (Table 1): 12 are primarily continental shelf species, and six are primarily continental slope species. Species were selected to represent a range of

** De novo composite design based on machine learning algorithm **

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Composites are widely used to create tunable materials to achieve superior mechanical properties. Brittle materials fail catastrophically in the presence of cracks. Incorporating softer constituents into brittle materials can alleviate stress concentration, leading to tougher

** Quantum machine learning **

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The realization of this hope depends on whether efficient quantum algorithms can be found for machine learning . A quantum algorithm is a set of instructions solving a problem, such as determining whether two graphs are isomorphic, that can be performed on a quantum

** On-line algorithms in machine learning **

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The areas of On-Line Algorithms and Machine Learning are both concerned with problems of making decisions about the present based only on knowledge of the past. Although these areas differ in terms of their emphasis and the problems typically studied, there are a

** Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition**

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Traffic signs are characterized by a wide variability in their visual appearance in real-world environments. For example, changes of illumination, varying weather conditions and partial occlusions impact the perception of road signs. In practice, a large number of different sign

** Coronary CT angiography derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling**

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Purpose To compare two technical approaches for determination of coronary computed tomography (CT) angiography derived fractional flow reserve (FFR) FFR derived from coronary CT angiography based on computational fluid dynamics (hereafter, FFRCFD) and

** Adversarial machine learning **

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such as avoiding detection of attacks, caus- ing benign input to be classified as attack input, launching focused or targeted attacks, or searching a classifier to find blind-spots in the algorithm . Adversarial machine learning is the design of machine learning algorithms that can

** Machine learning in wireless sensor networks: Algorithms strategies, and applications**

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Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine

** Machine learning : Trends, perspectives, and prospects**

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Broadly speaking, environments provide various resources to a learning algorithm and place constraints on those resources. Increasingly, machine learning researchers are formalizing these relationships, aiming to design algorithms that are provably effective in various

** Predicting catalytic activity of nanoparticles by a DFT-aided machine learning algorithm **

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Catalytic activities are often dominated by a few specific surface sites, and designing active sites is the key to realize high-performance heterogeneous catalysts. The great triumphs of modern surface science lead to reproduce catalytic reaction rates by modeling the

** Correlation-based feature selection of discrete and numeric class machine learning **

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Numeric Class Machine Learning Mark A. Hall MHALI, O CS. WAIKATO. AC. NZ Äepartment of Computer Science, University of Waikato, Hamilton, New Zealand Abstract Algorithms for feature selection fall into two broad categories: urappers that use the learning algorithm itself

** Data mining: practical machine learning tools and techniques with Java implementations**

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The final chapter deals with advanced topics such as visual machine learning text mining, and Web Thus, the number of data mining algorithms available on the Web site goes far beyond Similarly, the Apriori algorithm is introduced in a figure, but only in the Further Reading these definitions help to illustrate some of the general goals of feature-selection algorithms approaches, based on the relation between the selection scheme and the basic induction algorithm There are a number of different definitions in the machine learning literature for what

** Solar flare prediction using SDO/HMI vector magnetic field data with a machine learning algorithm **

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We attempt to forecast M-and X-class solar flares using a machine learning algorithm called support vector machine (SVM), and four years of data from the Solar Dynamics Observatorys Helioseismic and Magnetic Imager, the first instrument to continuously map

** Quantum algorithms for supervised and unsupervised machine learning **

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Machine learning tasks frequently involve problems of manipulating and classifying large numbers of vectors in high-dimensional spaces. Classical algorithms for solving such problems typically take time polynomial in the number of vectors and the dimension of the

** Foundations of machine learning **

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As in other areas of computer science, some critical measures of the quality of these algorithms are their time and space complexity. But, in machine learning we will need additionally a notion of sample complexity to evaluate the sample size required for the algorithm to learn aWith the increasing availability of electronic documents and the rapid growth of the World Wide Web, the task of automatic categorization of documents became the key method for organizing the information and knowledge discovery. Proper classification of e-documents

** Lifelong machine learning systems: Beyond learning algorithms **

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Abstract Lifelong Machine Learning or LML, considers systems that can learn many tasks from one or more domains over its lifetime. The goal is to sequentially retain learned knowledge and to selectively transfer that knowledge when learning a new task so as to

** Machine learning : the art and science of algorithms that make sense of data**

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As one of the most comprehensive machine learning texts around, this book does justice to the fields incredible richness, but without losing sight of the unifying principles. Peter Flachs clear, example-based approach begins by discussing how a spam filter works, which gives

**The boosting approach to machine learning : An overview**

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8. The Boosting Approach to Machine Learning : An Overview A very similar algorithm is described by Duffy and Helmbold [23] This is because both algorithms are doing a kind of functional gradient descent, an observation that is spelled out and exploited by Breiman , Duffy

** Machine learning for medical imaging**

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The naive Bayes algorithm is different from most machine learning algorithms in that one calculation is used to define the relationship between an input feature set and the output. As such, this method does not involve the same

** Practical feature subset selection for machine learning **

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This paper describes a new feature selection algorithm that uses a correlation based heuristic to determine the goodness of feature subsets, and evaluates its effectiveness with three common machine learning algorithms

** Weka: A machine learning workbench**

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who is extending and comparing algorithms . One exception- although still a library of modules-is Consultant , an expert system that allows domain experts to choose a learning algorithm suited to their needs. Consultant assumes that a machine learning algorithm exists

** Non-asymptotic analysis of stochastic approximation algorithms for machine learning **

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We consider the minimization of a convex objective function defined on a Hilbert space, which is only available through unbiased estimates of its gradients. This problem includes standard machine learning algorithms such as kernel logistic regression and least-squares