Supervised Learning

12 Key excerpts on "Supervised Learning"

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  The Fundamentals of algorithmic processes

  • Sourabh Pal(Author)
  • 2023(Publication Date)
  • Arcler Press

    (Publisher)

  One of the classic forms of this learning is the categorization issue. The learner must assimilate information to evaluate the performance of a function in a classification issue. The function displays a vector from many classes by considering various input-output specimens of the function. Machine Learning Algorithms 143 • UnSupervised Learning: These models a set of inputs. The instances that are labeled are not available. • Semi-Supervised Learning: A suitable function or classifier is produced as a result of combining the labeled and unlabeled instances in this learning process. • Reinforcement Learning: Given the observation of the world, in this case, the algorithm develops a strategy for how to carry out the task. In every action, there is a corresponding effect on the environment, and the environment offers feedback on these effects. The learning algorithm is then escorted by the feedback. • Transduction: This is like Supervised Learning, except that no function is expressly established. Transduction learning aims to predict new outcomes based on previously learned inputs and outputs, as well as fresh inputs. • Learning to Learn: The program develops its own inductive preference based on its prior experience with this type of problem. 6.2. Supervised Learning APPROACH In classification problems, Supervised Learning is quite popular because the purpose is to train a computer to study a classification system that has been built, which is quite common. A good example of this type of learning is digited recognition. Classification learning is appropriate for challenges in which the classification can be determined readily and in which presuming a classification is beneficial is assumed. Some situations where the mediator is able to resolve the classification problem on his or her own make it unnecessary to assign pre-determined classifications to each instance of a problem.

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  40 Algorithms Every Programmer Should Know

  Hone your problem-solving skills by learning different algorithms and their implementation in Python

  • Imran Ahmad(Author)
  • 2020(Publication Date)
  • Packt Publishing

    (Publisher)

  variables . Using a given dataset, a supervised machine learning algorithm is used to train a model that captures the complex relationship between the features and target variables represented by a mathematical formula. This trained model is the basic vehicle that is used for predictions.

  Predictions are made by generating the target variable of an unfamiliar set of features through the trained model.

  The ability to learn from existing data in Supervised Learning is similar to the ability of the human brain to learn from experience. This learning ability in Supervised Learning uses one of the attributes of the human brain and is a fundamental way of opening the gates to bring decision-making power and intelligence to machines.

  Let's consider an example where we want to use supervised machine learning techniques to train a model that can categorize a set of emails into legitimate ones (called legit ) and unwanted ones (called spam ). First of all, in order to get started, we need examples from the past so that the machine can learn what sort of content of emails should be classified as spam. This content-based learning task for text data is a complex process and is achieved through one of the supervised machine learning algorithms. Some examples of supervised machine learning algorithms that can be used to train the model in this example include decision trees and naive Bayes classifiers, which we will discuss later in this chapter.

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  Formulating supervised machine learning

  Before going deeper into the details of supervised machine learning algorithms, let's define some of the basic supervised machine learning terminologies:

  Terminology	Explanation
  Target variable	The target variable is the variable that we want our model to predict. There can be only one target variable

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  New Advances in Machine Learning

  • Yagang Zhang(Author)
  • 2010(Publication Date)
  • IntechOpen

    (Publisher)

  The task of the supervised learner is to predict the value of the function for any valid input object after having seen a number of training examples (i.e. pairs of input and target output). To achieve this, the learner has to generalize from the presented data to unseen situations in a reasonable way (see inductive bias). (Compare with unSupervised Learning.) Supervised Learning is a machine learning technique whereby the algorithm is first presented with training data which consists of examples which include both the inputs and the desired outputs; thus enabling it to learn a function. The learner should then be able to generalize from the presented data to unseen examples. by Mitchell (Mitchell, 2006). Supervised Learning also implies we are given a training set of (X, Y) pairs by a “teacher”. We know (sometimes only approximately) the values of f for the m samples in the training set,  we assume that if we can find a hypothesis, h , that closely agrees with f for the members of ؠ then this hypothesis will be a good guess for f especially if  is large. Curvefitting is a simple example of Supervised Learning of a function. Suppose we are given the values of a two-dimensional function. f , at the four sample points shown by the solid circles in Figure 9. We want to fit these four points with a function, h , drawn from the set, H , of second-degree functions. We show there a two-dimensional parabolic surface above the 1 x . 2 x , plane that fits the points. This parabolic function, h , is our hypothesis about the function f , which produced the four samples. In this case, h  f at the four samples, but we need not have required exact matches. Read more in section 3.1. Machine Learning Overview 15  Unsupervised Machine Learning: UnSupervised Learning 1 is a type of machine learning where manual labels of inputs are not used.

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  Machine Learning

  A First Course for Engineers and Scientists

  • Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, Thomas B. Schön(Authors)
  • 2022(Publication Date)
  • Cambridge University Press

    (Publisher)

  2 Supervised Learning A First Approach In this chapter, we will introduce the supervised machine learning problem as well as two basic machine learning methods for solving it. The methods we will introduce are called k -nearest neighbours and decision trees. These two methods are relatively simple, and we will derive them on intuitive grounds. Still, these methods are useful in their own right and are therefore a good place to start. Understanding their inner workings, advantages, and shortcomings also lays a good foundation for the more advanced methods that are to come in later chapters. 2.1 Supervised Machine Learning In supervised machine learning, we have some training data that contains examples of how some input 1 variable x relates to an output 2 variable y. By using some mathematical model or method, which we adapt to the training data, our goal is to predict the output y for a new, previously unseen, set of test data for which only x is known. We usually say that we learn (or train) a model from the training data, and that process involves some computations implemented on a computer. Learning from Labelled Data In most interesting supervised machine learning applications, the relationship between input x and output y is difficult to describe explicitly. It may be too cumbersome or complicated to fully unravel from application domain knowledge, or even unknown. The problem can therefore usually not be solved by writing a traditional computer program that takes x as input and returns y as output from a set of rules. The supervised machine learning approach is instead to learn the relationship between x and y from data, which contains examples of observed pairs of input and output values. In other words, supervised machine learning amounts to learning from examples. The data used for learning is called training data, and it has to consist of several input–output data points (samples) (x i , y i ), in total n of them.

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  Bayesian Reasoning and Machine Learning

  • David Barber(Author)
  • 2012(Publication Date)
  • Cambridge University Press

    (Publisher)

  Part III Machine learning Machine learning is fundamentally about extracting value from large datasets. Often the motivation is ultimately to produce an algorithm that can either mimic or enhance human/biological performance. In Part III we begin by discussing some of the basic concepts in machine learning, namely supervised and unSupervised Learning. We then move on to discuss some standard models in machine learning and related areas. 13 Machine learning concepts Machine learning is the body of research related to automated large-scale data analysis. Historically, the field was centred around biologically inspired models and the long-term goals of much of the community are oriented to producing models and algorithms that can process information as well as biological systems. The field also encompasses many of the traditional areas of statistics with, however, a strong focus on mathematical models and also prediction. Machine learning is now central to many areas of interest in computer science and related large-scale information processing domains. 13.1 Styles of learning • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Broadly speaking the main two subfields of machine learning are Supervised Learning and unsuper-vised learning . In Supervised Learning the focus is on accurate prediction, whereas in unSupervised Learning the aim is to find compact descriptions of the data. In both cases, one is interested in methods that generalise well to previously unseen data. In this sense, one distinguishes between data that is used to train a model and data that is used to test the performance of the trained model, see Fig.

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  Adversarial Machine Learning

  • Yevgeniy Vorobeychik, Murat Kantarcioglu, Yevgeniy Tu, Murat Shi(Authors)
  • 2022(Publication Date)
  • Springer

    (Publisher)

  5 C H A P T E R 2 Machine Learning Preliminaries To keep this book reasonably self-contained, we start with some machine learning basics. Ma- chine learning is often broadly divided into three major areas: Supervised Learning, unSupervised Learning, and reinforcement learning. While in practice these divisions are not always clean, they provide a good point of departure for our purposes. We start by offering a schematic representation of learning, shown in Figure 2.1. In this schematic representation, learning is viewed as a pipeline which starts with raw data, for ex- ample, a collection of executable files, with associated labels indicating whether a file is benign or malicious. This raw data is then processed to extract numeric features from each instance i , obtaining an associated feature vector x i (for example, this could be a collection of binary vari- ables indicating presence in an executable of particular system calls). This becomes processed data, but henceforth we call it simply data, as it is to this processed dataset that we can apply learning algorithms—the next step in the pipeline. Finally, the learning algorithm outputs a model, which may be a mathematical model of the data (such as its distribution) or a function that predicts labels on future instances. Raw Data Features Processed Data Learning Algorithm Model f Figure 2.1: A schematic view of machine learning. 2.1 Supervised Learning In Supervised Learning, you are given a model class F and a dataset D D fx i ; y i g n i D1 of feature vectors x i 2 X R m , where X is the feature space, and labels y i from some label set Y . This dataset is typically assumed to be generated i.i.d. from an unknown distribution P , i.e., .x i ; y i / P . The ultimate goal (the “holy grail”) is to find a model f 2 F with the property that E .x;y/ P OEl.f .x/; y/ E .x;y/ P OEl.f 0 .x/; y/ 8 f 0 2 F ; (2.1)

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  Fundamentals and Methods of Machine and Deep Learning

  Algorithms, Tools, and Applications

  • Pradeep Singh(Author)
  • 2022(Publication Date)
  • Wiley-Scrivener

    (Publisher)

  1 Pradeep Singh (ed.) Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools and Applications, (1–16) © 2022 Scrivener Publishing LLC 1 Supervised Machine Learning: Algorithms and Applications Shruthi H. Shetty*, Sumiksha Shetty † , Chandra Singh ‡ and Ashwath Rao § Department of ECE, Sahyadri College of Engineering & Management, Adyar, India Abstract The fundamental goal of machine learning (ML) is to inculcate computers to use data or former practice to resolve a specified problem. Artificial intelligence has given us incredible web search, self-driving vehicles, practical speech affirma- tion, and a massively better cognizance of human genetic data. An exact range of effective programs of ML already exist, which comprises classifiers to swot e-mail messages to study that allows distinguishing between unsolicited mail and non-spam messages. ML can be implemented as class analysis over super- vised, unsupervised, and reinforcement learning. Supervised ML (SML) is the subordinate branch of ML and habitually counts on a domain skilled expert who “teaches” the learning scheme with required supervision. It also generates a task that maps inputs to chosen outputs. SML is genuinely normal in characteriza- tion issues since the aim is to get the computer, familiar with created descrip- tive framework. The data annotation is termed as a training set and the testing set as unannotated data. When annotations are discrete in the value, they are called class labels and continuous numerical annotations as continuous target values. The objective of SML is to form a compact prototype of the distribution of class labels in terms of predictor types. The resultant classifier is then used to designate class labels to the testing sets where the estimations of the predictor types are known, yet the values of the class labels are unidentified. Under certain assumptions, the larger the size of the training set, the better the expectations on the test set.

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  Introduction To Algorithms

  • Rex Porbasas Flejoles(Author)
  • 2019(Publication Date)
  • Arcler Press

    (Publisher)

  The classification problem is one of the standard formulations of this learning. In classification problem the student is required to absorb, to estimate the performance of a function. Out of several classes, the function plots a vector into one by considering several input-output specimens of the function. ii. UnSupervised Learning: A set of inputs is modeled by unSupervised Learning. The labeled examples are not available. iii. Semi-Supervised Learning: This learning combines the labeled and the unlabeled examples to produce a suitable function or classifier. iv. Reinforcement learning: Given the surveillance of world here the algorithm acquires a strategy of how to perform. Every action has a certain effect on the environment, of which the feedback is provided by the environment. The feedback then escorts the learning algorithm. v. Transduction: This is just like Supervised Learning, but here a function is not created explicitly. The Transduction learning tries to forecast new outcomes based on the trained inputs- outputs and the new inputs. vi. Learning to learn: It is based on the earlier experience in this type the algorithm acquires its own inductive preference. Types of Machine Learning Algorithms 81 4.2. Supervised Learning APPROACH In the classification problems Supervised Learning is quite common because of the goal, which is getting a computer to study a classification system created. Digit recognition is an example of this learning. Classification learning is suitable for the problems where the classification is determined easily and assuming a classification is helpful. In some of the cases, if the mediator itself can solve out the classification then it is not necessary to provide pre-determined classifications to each occasion of a problem. In a classification framework, this is the example of an unSupervised Learning. In Supervised Learning, there is a probability that the inputs are often left undefined.

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  50 Algorithms Every Programmer Should Know

  An unbeatable arsenal of algorithmic solutions for real-world problems

  • Imran Ahmad(Author)
  • 2023(Publication Date)
  • Packt Publishing

    (Publisher)

  labels . These features often comprise structured data like user profiles, historical sales figures, or sensor measurements, while the labels usually represent specific outcomes we want to predict, such as customer purchasing habits or product quality ratings. Using a given dataset, a supervised machine learning algorithm is used to train a model that captures the complex relationship between the features and labels represented by a mathematical formula. This trained model is the basic vehicle that is used for predictions.

  The ability to learn from existing data in Supervised Learning is similar to the ability of the human brain to learn from experience. This learning ability in Supervised Learning uses one of the attributes of the human brain and is a fundamental way of opening the gates to bring decision-making power and intelligence to machines.

  Let’s consider an example where we want to use supervised machine learning techniques to train a model that can categorize a set of emails into legitimate ones (called legit ) and unwanted ones (called spam ). In order to get started, we need examples from the past so that the machine can learn what sort of content of emails should be classified as spam.

  This content-based learning task using text data is a complex process and is achieved through one of the supervised machine learning algorithms. Some examples of supervised machine learning algorithms that can be used to train the model in this example include decision trees and Naive Bayes classifiers, which we will discuss later in this chapter.

  For now, we will focus on how we can formulate supervised machine learning problems.

  Formulating supervised machine learning problems

  Before going deeper into the details of supervised machine learning algorithms, let’s define some of the basic supervised machine learning terminology:

  Terminology	Explanation
  Label	A label is the variable that our model is tasked with predicting. There can be only one label in a supervised machine learning model.

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  Artificial Intelligence in Wireless Robotics

  • Kwang-Cheng Chen(Author)
  • 2020(Publication Date)
  • River Publishers

    (Publisher)

  Assisted by these trained models, machine learning can then be used for classification, prediction and decision making. 53 54 Machine Learning Basics Figure 3.1 Visualization of Election Fingerprint [1]. • Strategy : The criteria for training mathematical models are called strate-gies. How to select an appropriate strategy is closely associated with training data. Empirical risk minimization (ERM) and structural risk minimization (SRM) are two fundamental strategic issues, where the lat-ter can beneficially avoid the over-fitting phenomenon when the sample size is small. • Algorithm : Algorithms are constructed to solve unknown parameters based on the determined model and selected strategy, which can be viewed as an optimization process. A good algorithm can not only yield a globally optimal solution, but also has low computational complexity and storage complexity. Statistical learning theory was introduced in the late 1960’s and is con-sidered as a branch of mathematical statistical analysis treating the problem of function estimation from a given collection of data. Particularly since the invention of widely applied support vector machines (SVMs) in the mid-1990’s, statistical learning theory has been shown to be useful to develop new learning algorithms. 3.1 Supervised Learning 55 3.1 Supervised Learning If machine learning proceeds as there is a teacher to supply feedback for the model or the algorithm, it is called Supervised Learning . In other words, a training dataset is typically available for Supervised Learning. 3.1.1 Regression Regression analysis can be viewed as a kind of statistical process method for estimating the relationships among variables. Relying on modeling the function relationship between a dependent variable (objective) and one or more independent variables (predictors), regression is a powerful statistical tool for predicting and forecasting a continuous-valued objective given a set of predictors.

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  Artificial Intelligence

  Foundations of Computational Agents

  • David L. Poole, Alan K. Mackworth(Authors)
  • 2010(Publication Date)
  • Cambridge University Press

    (Publisher)

  This is called classification when the target variables are discrete and regres- sion when the target features are continuous. Other learning tasks include learning classifications when the examples are not already classified (unSupervised Learning), learning what to do based on rewards and punishments (reinforcement learning), learning to reason faster (analytic learning), and learning richer representations such as logic programs (inductive logic programming) or Bayesian networks. Feedback Learning tasks can be characterized by the feedback given to the learner. In Supervised Learning, what has to be learned is specified for each 7.1. Learning Issues 285 example. Supervised classification occurs when a trainer provides the clas- sification for each example. Supervised Learning of actions occurs when the agent is given immediate feedback about the value of each action. Unsu- pervised learning occurs when no classifications are given and the learner must discover categories and regularities in the data. Feedback often falls between these extremes, such as in reinforcement learning, where the feed- back in terms of rewards and punishments occurs after a sequence of actions. This leads to the credit-assignment problem of determining which actions were responsible for the rewards or punishments. For example, a user could give rewards to the delivery robot without telling it exactly what it is being rewarded for. The robot then must either learn what it is being rewarded for or learn which actions are preferred in which situations. It is possible that it can learn what actions to perform without actually determining which con- sequences of the actions are responsible for rewards. Representation For an agent to use its experiences, the experiences must affect the agent’s internal representation. Much of machine learning is studied in the context of particular representations (e.g., decision trees, neural net- works, or case bases).

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  MATLAB Machine Learning

  • Michael Paluszek, Stephanie Thomas(Authors)
  • 2016(Publication Date)
  • Apress

    (Publisher)

  PART I Introduction to Machine Learning CHAPTER 1 An Overview of Machine Learning 1.1 Introduction Machine learning is a field in computer science where existing data are used to predict, or respond to, future data. It is closely related to the fields of pattern recognition, computational statistics, and artificial intelligence. Machine learning is important in areas like facial recognition, spam filtering, and others where it is not feasible, or even possible, to write algorithms to perform a task. For example, early attempts at spam filtering had the user write rules to determine what was spam. Your success depended on your ability to correctly identify the attributes of the message that would categorize an email as spam, such as a sender address or subject keyword, and the time you were willing to spend to tweak your rules. This was only moderately successful as spam generators had little difficulty anticipating people’s rules. Modern systems use machine learning techniques with much greater success. Most of us are now familiar with the concept of simply marking a given message as “spam” or “not spam,” and we take for granted that the email system can quickly learn which features of these emails identify them as spam and prevent them from appearing in our inbox. This could now be any combination of IP or email addresses and keywords in the subject or body of the email, with a variety of matching criteria. Note how the machine learning in this example is data-driven, autonomous, and continuously updating itself as you receive email and flag it. In a more general sense, what does machine learning mean? Machine learning can mean using ma-chines (computers and software) to gain meaning from data. It can also mean giving machines the ability to learn from their environment. Machines have been used to assist humans for thousands of years. Con-sider a simple lever, which can be fashioned using a rock and a length of wood, or the inclined plane.

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