We provide an introduction to theoretical and practical aspects of Boosting and Ensemble learning, providing a useful reference for researchers in the field of Boosting as well as for those seeking to enter this fascinating area of research. We begin with a short background concerning the necessary learning theoretical foundations of weak learners and their linear combinations. We then point out the useful connection between Boosting and the Theory of Optimization, which facilitates the understanding of Boosting and later on enables us to move on to new Boosting algorithms, applicable to a broad spectrum of problems. In order to increase the relevance of the paper to practitioners, we have added remarks, pseudo code, “tricks of the trade”, and algorithmic considerations where appropriate. Finally, we illustrate the usefulness of Boosting algorithms by giving an overview of some existing applications. The main ideas are illustrated on the problem of binary classification, although several extensions are discussed.
These lecture notes are intended to give a tutorial introduction to the formulation and analysis of reinforcement learning problems. In these problems, an agent chooses actions to take in some environment, aiming to maximize a reward function. Many control, scheduling, planning and game-playing tasks can be formulated in this way, as problems of controlling a Maxkov decision process. We review the classical dynamic programming approaches to finding optimal controllers. For large state spaces, these techniques are impractical. We review methods based on approximate value functions, estimated via simulation. In particular, we discuss the motivation for (and shortcomings of) the TD (lambda) algorithm.
In these notes our aim is to survey recent (and not so recent) results regarding the mathematical foundations of learning theory. The focus in this article is on the theoretical side and not on the applicative one; hence, we shall not present examples which may be interesting from the practical point of view but have little theoretical significance. This survey is far from being complete and it focuses on problems the author finds interesting (an opinion which is not necessarily shared by the majority of the learning community). Relevant books which present a more evenly balanced approach are, for example 1, 4, 34, 35
Bayesian methods allow for a simple and intuitive representation of the function spaces used by kernel methods. This chapter describes the basic principles of Gaussian Processes, their implementation and their connection to other kernel-based Bayesian estimation methods, such as the Relevance Vector Machine.
We briefly describe the main ideas of statistical learning theory, support vector machines, and kernel feature spaces. This includes a derivation of the support vector optimization problem for classification and regression, the v-trick, various kernels and an overview over applications of kernel methods.
This tutorial discusses some knowledge representation issues in machine learning. The focus is on machine learning applications for which the individuals that are the subject of learning have complex structure. To represent such individuals, a rich knowledge representation language based on higher-order logic is introduced. The logic is also employed to construct comprehensible hypotheses that one might want to learn about the individuals. The tutorial introduces the main ideas of this approach to knowledge representation in a mostly informal way and gives a number of illustrations. The application of the ideas to decision-tree learning is also illustrated with an example.
This paper surveys some basic techniques and recent results related to online learning.Our focus is on linear classification.The most familiar algorithm for this task is the perceptron.We explain the perceptron algorithm and its convergence proof as an instance of a generic method based on Bregman divergences.This leads to a more general algorithm known as the p -norm perceptron.We give the proof for generalizing the perceptron convergence theorem for the p -norm perceptron and the non-separable case.We also show how regularization,again based on Bregman divergences,can make an online algorithm more robust against target movement.
Association rules are "if-then rules" with two measures which quantify the support and confidence of the rule for a given data set. Having their origin in market basked analysis, association rules are now one of the most popular tools in data mining. This popularity is to a large part due to the availability of efficient algorithms following from the development of the Apriori algorithm.We will review the basic Apriori algorithm and discuss variants for distributed data, inclusion of constraints and data taxonomies. The review ends with an outlook on tools which have the potential to deal with long itemsets and considerably reduce the amount of (uninteresting) itemsets returned. The discussion will focus on the problem of finding frequent itemsets.
