In this chapter, we present the main classic machine learning methods. A large part of the chapter is devoted to supervised learning techniques for classification and regression, including nearest-neighbor methods, linear and logistic regressions, support vector machines and tree-based algorithms. We also describe the problem of overfitting as well as strategies to overcome it. We finally provide a brief overview of unsupervised learning methods, namely for clustering and dimensionality reduction
International audienceDesigning Machine Learning algorithms implies to answer three main questions: ...
Abstract Supervised learning accounts for a lot of research activity in machine learning and many su...
One of the major objectives of machine learning is to instruct computers to use data or past experie...
International audienceIn this chapter, we present the main classic machine learning methods. A large...
The machine learning field, which can be briefly defined as enabling computers make successful predi...
Machine learning algorithms are used to train the machine to learn on its own and improve from exper...
Abstract: Machine learning is important because it gives us accurate predictions based on data. It c...
This chapter aims to introduce the common methods and practices of statistical machine learning tech...
In this contribution, we provide a basic introduction to key concepts of Machine Learning (ML). ML c...
The dissertation deals with clustering algorithms and transforming regression prob-lems into classif...
Machine learning problems of supervised classification, unsupervised clustering and parsimonious app...
The purpose of this study is to briefly learn the theory and implementation of three most commonly u...
A large number of techniques has been developed so far to tell the diversity of machine learning. Ma...
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic a...
Abstract: Machine Learning generates programs that make predictions and informed decisions about com...
International audienceDesigning Machine Learning algorithms implies to answer three main questions: ...
Abstract Supervised learning accounts for a lot of research activity in machine learning and many su...
One of the major objectives of machine learning is to instruct computers to use data or past experie...
International audienceIn this chapter, we present the main classic machine learning methods. A large...
The machine learning field, which can be briefly defined as enabling computers make successful predi...
Machine learning algorithms are used to train the machine to learn on its own and improve from exper...
Abstract: Machine learning is important because it gives us accurate predictions based on data. It c...
This chapter aims to introduce the common methods and practices of statistical machine learning tech...
In this contribution, we provide a basic introduction to key concepts of Machine Learning (ML). ML c...
The dissertation deals with clustering algorithms and transforming regression prob-lems into classif...
Machine learning problems of supervised classification, unsupervised clustering and parsimonious app...
The purpose of this study is to briefly learn the theory and implementation of three most commonly u...
A large number of techniques has been developed so far to tell the diversity of machine learning. Ma...
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic a...
Abstract: Machine Learning generates programs that make predictions and informed decisions about com...
International audienceDesigning Machine Learning algorithms implies to answer three main questions: ...
Abstract Supervised learning accounts for a lot of research activity in machine learning and many su...
One of the major objectives of machine learning is to instruct computers to use data or past experie...