Classic machine learning methods

• URL: http://arxiv.org/abs/2310.11470v1
• Date: Wed, 24 May 2023 13:38:38 GMT
• Title: Classic machine learning methods
• Authors: Johann Faouzi and Olivier Colliot
• Abstract summary: A large part of the chapter is devoted to supervised learning techniques for classification and regression. We also describe the problem of overfitting as well as strategies to overcome it.
• Score: 5.085743099113423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• A Unified Framework for Neural Computation and Learning Over Time [56.44910327178975]
  Hamiltonian Learning is a novel unified framework for learning with neural networks "over time" It is based on differential equations that: (i) can be integrated without the need of external software solvers; (ii) generalize the well-established notion of gradient-based learning in feed-forward and recurrent networks; (iii) open to novel perspectives.
  arXiv  Detail & Related papers  (2024-09-18T14:57:13Z)
• Topological Methods in Machine Learning: A Tutorial for Practitioners [4.297070083645049]
  Topological Machine Learning (TML) is an emerging field that leverages techniques from algebraic topology to analyze complex data structures. This tutorial provides a comprehensive introduction to two key TML techniques, persistent homology and the Mapper algorithm. To enhance accessibility, we adopt a data-centric approach, enabling readers to gain hands-on experience applying these techniques to relevant tasks.
  arXiv  Detail & Related papers  (2024-09-04T17:44:52Z)
• Introduction to Machine Learning [0.0]
  This book introduces the mathematical foundations and techniques that lead to the development and analysis of many of the algorithms that are used in machine learning. The subject then switches to generative methods, starting with a chapter that presents sampling methods. The next chapters focus on unsupervised learning methods, for clustering, factor analysis and manifold learning.
  arXiv  Detail & Related papers  (2024-09-04T12:51:41Z)
• Semi-Supervised Machine Learning: a Homological Approach [0.0]
  We describe the mathematical foundations of a new approach to semi-supervised Machine Learning. Using Symbolic Computation and Computer Algebra, we apply the concept of persistent homology to obtain a new semi-supervised learning method.
  arXiv  Detail & Related papers  (2023-01-27T11:16:45Z)
• A Survey on Deep Semi-supervised Learning [51.26862262550445]
  We first present a taxonomy for deep semi-supervised learning that categorizes existing methods. We then offer a detailed comparison of these methods in terms of the type of losses, contributions, and architecture differences.
  arXiv  Detail & Related papers  (2021-02-28T16:22:58Z)
• Functorial Manifold Learning [1.14219428942199]
  We first characterize manifold learning algorithms as functors that map pseudometric spaces to optimization objectives. We then use this characterization to prove refinement bounds on manifold learning loss functions and construct a hierarchy of manifold learning algorithms. We express several popular manifold learning algorithms as functors at different levels of this hierarchy, including Metric Multidimensional Scaling, IsoMap, and UMAP.
  arXiv  Detail & Related papers  (2020-11-15T02:30:23Z)
• A Survey on Large-scale Machine Learning [67.6997613600942]
  Machine learning can provide deep insights into data, allowing machines to make high-quality predictions. Most sophisticated machine learning approaches suffer from huge time costs when operating on large-scale data. Large-scale Machine Learning aims to learn patterns from big data with comparable performance efficiently.
  arXiv  Detail & Related papers  (2020-08-10T06:07:52Z)
• Reinforcement Learning as Iterative and Amortised Inference [62.997667081978825]
  We use the control as inference framework to outline a novel classification scheme based on amortised and iterative inference. We show that taking this perspective allows us to identify parts of the algorithmic design space which have been relatively unexplored.
  arXiv  Detail & Related papers  (2020-06-13T16:10:03Z)
• Deep Learning Techniques for Inverse Problems in Imaging [102.30524824234264]
  Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems. We present a taxonomy that can be used to categorize different problems and reconstruction methods.
  arXiv  Detail & Related papers  (2020-05-12T18:35:55Z)
• There and Back Again: Revisiting Backpropagation Saliency Methods [87.40330595283969]
  Saliency methods seek to explain the predictions of a model by producing an importance map across each input sample. A popular class of such methods is based on backpropagating a signal and analyzing the resulting gradient. We propose a single framework under which several such methods can be unified.
  arXiv  Detail & Related papers  (2020-04-06T17:58:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.