Classic Machine Learning Methods
نویسندگان
چکیده
Abstract In this chapter, we present the main classic machine learning methods. A large part of chapter is devoted to supervised techniques for classification and regression, including nearest neighbor methods, linear logistic regressions, support vector machines, tree-based algorithms. We also describe problem overfitting as well strategies overcome it. finally provide a brief overview unsupervised namely, clustering dimensionality reduction. The does not cover neural networks deep these will be presented in Chaps. 3 , 4 5 6 .
منابع مشابه
On Feature Subset Selection for Fuzzy and Classic Machine Learning Classification Methods
Feature subset selection supports the classification task by reducing the search space as well as by removing irrelevant and random features, which might compromise the resulting classification model. Decision trees perform an embedded feature selection as they select only the relevant features for the splitting of the datasets during the induction process. FUZZYDT is a fuzzy decision tree whic...
متن کاملComparison of classic regression methods with neural network and support vector machine in classifying groundwater resources
In the present era, classification of data is one of the most important issues in various sciences in order to detect and predict events. In statistics, the traditional view of these classifications will be based on classic methods and statistical models such as logistic regression. In the present era, known as the era of explosion of information, in most cases, we are faced with data that c...
متن کاملKernel Methods in Machine Learning
We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowi...
متن کاملEnsemble Methods in Machine Learning
Ensemble methods are learning algorithms that construct a set of classi ers and then classify new data points by taking a weighted vote of their predictions The original ensemble method is Bayesian aver aging but more recent algorithms include error correcting output coding Bagging and boosting This paper reviews these methods and explains why ensembles can often perform better than any single ...
متن کاملMachine learning methods in chemoinformatics
Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure-activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neuromethods
سال: 2023
ISSN: ['1940-6045', '0893-2336']
DOI: https://doi.org/10.1007/978-1-0716-3195-9_2