Maximum Margin Clustering

TL;DR: Overall, using multiple kernels instead of a single one is useful and it is believed that combining kernels in a nonlinear or data-dependent way seems more promising than linear combination in fusing information provided by simple linear kernels, whereas linear methods are more reasonable when combining complex Gaussian kernels.

TL;DR: This review paper begins at the definition of clustering, takes the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyzes the clustered algorithms from two perspectives, the traditional ones and the modern ones.

TL;DR: This work presents DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features and outperforms the current state of the art by a significant margin on all the standard benchmarks.

TL;DR: This paper proposes a novel clustering model to learn the data similarity matrix and clustering structure simultaneously and derives an efficient algorithm to optimize the proposed challenging problem, and shows the theoretical analysis on the connections between the method and the K-means clustering, and spectral clustering.

TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.

TL;DR: A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well.

TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms.

Abstract: Chapters 2–7 make up Part II of the book: artificial neural networks. After introducing the basic concepts of neurons and artificial neuron learning rules in Chapter 2, Chapter 3 describes a particular formalism, based on signal-plus-noise, for the learning problem in general. After presenting the basic neural network types this chapter reviews the principal algorithms for error function minimization/optimization and shows how these learning issues are addressed in various supervised models. Chapter 4 deals with issues in unsupervised learning networks, such as the Hebbian learning rule, principal component learning, and learning vector quantization. Various techniques and learning paradigms are covered in Chapters 3–6, and especially the properties and relative merits of the multilayer perceptron networks, radial basis function networks, self-organizing feature maps and reinforcement learning are discussed in the respective four chapters. Chapter 7 presents an in-depth examination of performance issues in supervised learning, such as accuracy, complexity, convergence, weight initialization, architecture selection, and active learning. Par III (Chapters 8–15) offers an extensive presentation of techniques and issues in evolutionary computing. Besides the introduction to the basic concepts in evolutionary computing, it elaborates on the more important and most frequently used techniques on evolutionary computing paradigm, such as genetic algorithms, genetic programming, evolutionary programming, evolutionary strategies, differential evolution, cultural evolution, and co-evolution, including design aspects, representation, operators and performance issues of each paradigm. The differences between evolutionary computing and classical optimization are also explained. Part IV (Chapters 16 and 17) introduces swarm intelligence. It provides a representative selection of recent literature on swarm intelligence in a coherent and readable form. It illustrates the similarities and differences between swarm optimization and evolutionary computing. Both particle swarm optimization and ant colonies optimization are discussed in the two chapters, which serve as a guide to bringing together existing work to enlighten the readers, and to lay a foundation for any further studies. Part V (Chapters 18–21) presents fuzzy systems, with topics ranging from fuzzy sets, fuzzy inference systems, fuzzy controllers, to rough sets. The basic terminology, underlying motivation and key mathematical models used in the field are covered to illustrate how these mathematical tools can be used to handle vagueness and uncertainty. This book is clearly written and it brings together the latest concepts in computational intelligence in a friendly and complete format for undergraduate/postgraduate students as well as professionals new to the field. With about 250 pages covering such a wide variety of topics, it would be impossible to handle everything at a great length. Nonetheless, this book is an excellent choice for readers who wish to familiarize themselves with computational intelligence techniques or for an overview/introductory course in the field of computational intelligence. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond—Bernhard Schölkopf and Alexander Smola, (MIT Press, Cambridge, MA, 2002, ISBN 0-262-19475-9). Reviewed by Amir F. Atiya.