Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model- agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal effects and explaining individual predictions with Shapley values and LIME.
標(biāo)簽： interpretable-machine-learning
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
Much has been written concerning the manner in which healthcare is changing, with a particular emphasis on how very large quantities of data are now being routinely collected during the routine care of patients. The use of machine learning meth- ods to turn these ever-growing quantities of data into interventions that can improve patient outcomes seems as if it should be an obvious path to take. However, the field of machine learning in healthcare is still in its infancy. This book, kindly supported by the Institution of Engineering andTechnology, aims to provide a “snap- shot” of the state of current research at the interface between machine learning and healthcare.
標(biāo)簽： Technologies Healthcare Learning Machine
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
Machine learning is about designing algorithms that automatically extract valuable information from data. The emphasis here is on “automatic”, i.e., machine learning is concerned about general-purpose methodologies that can be applied to many datasets, while producing something that is mean- ingful. There are three concepts that are at the core of machine learning: data, a model, and learning.
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
標(biāo)簽： Introduction Classifiers Algorithms introduces
上傳時(shí)間： 2015-10-20
上傳用戶：aeiouetla
Simple GA code (Pascal code from Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization, and machine learning.)
標(biāo)簽： D. E. code Optimization
上傳時(shí)間： 2014-12-07
上傳用戶：wlcaption
a Java toolkit for training, testing, and applying Bayesian Network Classifiers. Implemented classifiers have been shown to perform well in a variety of artificial intelligence, machine learning, and data mining applications.
標(biāo)簽： Classifiers Implemented Bayesian applying
上傳時(shí)間： 2015-09-11
上傳用戶：ommshaggar
ApMl provides users with the ability to crawl the web and download pages to their computer in a directory structure suitable for a machine learning system to both train itself and classify new documents. Classification Algorithms include Naive Bayes, KNN
標(biāo)簽： the provides computer download
上傳時(shí)間： 2015-11-29
上傳用戶：ywqaxiwang
一個(gè)用神經(jīng)網(wǎng)絡(luò)方法實(shí)現(xiàn)人臉識(shí)別的程序，來源于CMU的machine learning 課程作業(yè)，具有參考價(jià)值
標(biāo)簽： 神經(jīng)網(wǎng)絡(luò) 人臉識(shí)別 程序
上傳時(shí)間： 2013-11-28
上傳用戶：515414293
Recent advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique’s potential.
標(biāo)簽： experimental generation advances enormous
上傳時(shí)間： 2016-10-23
上傳用戶：wkchong
Many of the pattern fi nding algorithms such as decision tree, classifi cation rules and clustering techniques that are frequently used in data mining have been developed in machine learning research community. Frequent pattern and association rule mining is one of the few excep- tions to this tradition. The introduction of this technique boosted data mining research and its impact is tremendous. The algorithm is quite simple and easy to implement. Experimenting with Apriori-like algorithm is the fi rst thing that data miners try to do.
標(biāo)簽： 64257 algorithms decision pattern
上傳時(shí)間： 2014-01-12
上傳用戶：wangdean1101
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