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A Review on the Different Regression Analysis in Supervised Learning
Published in K Hemachandran, Shubham Tayal, Preetha Mary George, Parveen Singla, Utku Kose, Bayesian Reasoning and Gaussian Processes for Machine Learning Applications, 2022
K Sudhaman, Mahesh Akuthota, Sandip Kumar Chaurasiya
In polynomial regression, the relation between the continuous parameter (y) and the predictor parameter (x) is designed as something of an nth dimension polynomial. It deals with a nonlinear data set using a linear model. It is tantamount to multiple linear regression. When the linear regression model is unable to capture the pattern of nonlinear data set, the problem of underfitting arises, where an underfit machine learning model will have underperformed on the training data. To avoid this, an underfit polynomial regression is used for nonlinear data which fits the nonlinear relationship between the value of x, which is the independent variable and the values of the dependent variables of y, which is the target variable more precisely. (“Machine Learning Polynomial Regression - Javatpoint” 2020) (Figure 2.8).
A Deep Learning-based System for Network Cyber Threat Detection
Published in Brij B. Gupta, Michael Sheng, Machine Learning for Computer and Cyber Security, 2019
Angel Luis Perales Gomez, Lorenzo Fernandez Maimo, Felix J. Garcia Clemente
For years, machine learning has been used in a wide range of applications, such as spam filters or recommendation systems. Nowadays, machine learning is being replaced with more advanced deep learning techniques. Both are closely related and share the same philosophy—they build a model from an input dataset and use it to make predictions on unseen data. Indeed, deep learning is considered as a subfield of machine learning (Fig. 1). However, machine learning needs feature engineering to generate the input features, thus requiring more domain expertise, whereas deep learning obtains their own features from raw data [12]. This fact, together with the increase of computation power in modern hardware and the availability of public datasets to be used in a wide domain of problems, are the main reasons of deep learning success.
Artificial Intelligence Software and Hardware Platforms
Published in Mazin Gilbert, Artificial Intelligence for Autonomous Networks, 2018
Rajesh Gadiyar, Tong Zhang, Ananth Sankaranarayanan
In machine learning, learning algorithms build models from data. These models can continuously improve as they are exposed to more data over time. There are four main types of machine learning: supervised, unsupervised, reinforcement, and continuous learning (see Chapter 2). In supervised machine learning, the algorithm learns to identify data by processing and categorizing vast quantities of labeled data. In unsupervised machine learning, the algorithm identifies patterns and categories within large amounts of unlabeled data. Reinforcement learning allows the machine or software agent to learn its behavior based on feedback from the environment. And continuous learning (also called lifelong learning) is built on the idea of learning continuously and adaptively about the external world.
Wireless Network Design Optimization for Computer Teaching with Deep Reinforcement Learning Application
Published in Applied Artificial Intelligence, 2023
Reinforcement learning is a branch of machine learning that focuses on how to act based on feedback from the environment in order to achieve the desired benefit. In psychology, it is based on the theory of behaviorism, which explains how organisms gradually create expectation of stimuli under the influence of rewards or punishments and develop regular behaviors that maximize their benefits. There are several components to the reinforcement learning model, the most basic of which being a set of states in the environment, a set of actions, rules for transitions between states, and immediate rewards following state transitions. The subject and environment of reinforcement learning interact at discrete time steps, and at each time, the subject observes a corresponding piece of information. It usually contains reward information in it, and then it selects an action from a set of actions. Executed in the environment, the environment transitions to a new state, and then gets a reward associated with this transition. The goal of reinforcement learning agents is to get as many rewards as possible. The power of reinforcement learning comes from two aspects, one is to use the past experience of the subject as a sample to optimize the behavior, and the other is to use the function approximation to simulate the complex system environment. Therefore, reinforcement learning methods are universal and have been studied in many other fields.
A Systematic Review of Recent Machine Learning Techniques for Plant Disease Identification and Classification
Published in IETE Technical Review, 2023
Considerable studies and surveys have been performed in the past few years to compare the analysis of classification methods in machine learning. The branch of machine learning is broadly classified into supervised and unsupervised learning [23]. Supervised learning deals with the labeled data, where the machine is trained by providing input and their corresponding correct output, and it uses this “labeled” data to predict the output of unknown input. Classification and Regression Problems come under Supervised learning. In contrast, Unsupervised Learning learns from experience. The idea behind unsupervised learning is to map a pattern or structure from the unlabeled data without any supervision. Further Association and Clustering are the categories of unsupervised learning. The comparative study shows that the Convolution Neural network (CNN) [34,35], K-Nearest Neighbor(K-NN) [36,37], Artificial Neural Network (ANN) [33,38], and Support Vector Machine (SVM) [39,40] are the ones that are used at most and provide accurate prediction for the Classification of plant disease type. Further Table 3 provides a comparative analysis that explains four majorly used Machine learning techniques to classify crop diseases. It provides a brief review of each machine learning classification model used for researching different crops and the details of the data set on which the research study is performed, and their corresponding classification results based on various performance measures.
A comparative study between PCR, PLSR, and LW-PLS on the predictive performance at different data splitting ratios
Published in Chemical Engineering Communications, 2022
Parametric and non-parametric algorithms differ as the latter does not require the estimation of distribution parameters such as mean and standard deviation to obtain an algorithm (Scheff 2016; King and Eckersley 2019). Non-parametric models are generally less powerful due to the lack of supporting evidence when making conclusions on the target function (Scheff 2016). Building a model is not only dependent on the assumptions placed upon it, but there are also different types of learning methods. Types of machine learning include supervised, unsupervised, semi-supervised and reinforcement learning. For supervised learning, the machine learns the target function, to determine a correlation between known input and output variables. Supervised learning algorithms can be subdivided into regression and classification tasks depending on the objectives (Haimi et al. 2013; Vieira et al. 2020). The difference between regression and classification is the former predicts continuous values while the latter is used for categorizing input data. Examples of supervised learning techniques include multiple linear regression, PCR, PLSR, and LW-PLS (Ambika 2019; Chiplunkar and Huang 2019; Thomas 2019; Ibrahim et al. 2020).