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Inside this Book – Let us begin our mathematical analysis by showing how successful learning can be achieved in a relatively simplified setting. Imagine you have just arrived in some small Pacific island. You soon find out that papayas are a significant ingredient in the local diet. However, you have never before tasted papayas. You have to learn how to predict whether a papaya you see in the market is tasty or not. First, you need to decide which features of a papaya your prediction should be based on. On the basis of your previous experience with other fruits, you decide to use two features: the papaya’s color, ranging from dark green, through orange and red to dark brown, and the papaya’s softness, ranging from rock hard to mushy. Your input for figuring out your prediction rule is a sample of papayas that you have examined for color and softness and then tasted and found out whether they were tasty or not. Let us analyze this task as a demonstration of the considerations involved in learning problems. Our first step is to describe a formal model aimed to capture such learning tasks.
Inside this book –Understanding Machine Learning PDF Book by Shai Shalev Shwartz – In this chapter we will study the family of linear predictors, one of the most useful families of hypothesis classes. Many learning algorithms that are being widely used in practice rely on linear predictors, first and foremost because of the ability to learn them efficiently in many cases. In addition, linear predictors are intuitive, are easy to interpret, and fit the data reasonably well in many natural learning problems. We will introduce several hypothesis classes belonging to this family – halfspaces, linear regression predictors, and logistic regression predictors – and present rele- vant learning algorithms: linear programming and the Perceptron algorithm for the class of halfspaces and the Least Squares algorithm for linear regression. This chapter is focused on learning linear predictors using the ERM approach; however, in later chapters we will see alternative paradigms for learning these hypothesis classes.
Understanding Machine Learning by Shai Shalev Shwartz PDF : eBook Information
- Full Book Name – Understanding Machine Learning
- Author of this Book -Shai Shalev Shwartz
- Language – English
- Book Genre – Non-Fiction, Science
- Download Format – PDF
- Size – 2.5 MB
- eBook Pages – 449
- Price – Free