From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.

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Nonlinear dimensionality reduction by locally linear embedding.

From the Publisher:
With this text, you gain an understanding of the fundamental concepts of algorithms, the very heart of computer science. It introduces the basic data structures and programming techniques often used in efficient algorithms. Covers use of lists, push-down stacks, queues, trees, and graphs. Later chapters go into sorting, searching and graphing algorithms, the string-matching algorithms, and the Schonhage-Strassen integer-multiplication algorithm. Provides numerous graded exercises at the end of each chapter.


0201000296B04062001

The Design and Analysis of Computer Algorithms

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

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Community detection in graphs

This paper considers a generalization, called the Shannon switching game on vertices, of a familiar board game called Hex. It is shown that determining who wins such a game if each player plays perfectly is very hard; in fact, if this game problem is solvable in polynomial time, then any problem solvable in polynomial space is solvable in polynomial time. This result suggests that the theory of combinational games is difficult.

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A Combinatorial Problem Which Is Complete in Polynomial Space

In this paper we explore the use of 2-3 trees to represent sorted lists. We analyze the worst-case cost of sequences of insertions and deletions in 2-3 trees under each of the following three assumptions: (i) only insertions are performed; (ii) only deletions are performed; (iii) deletions occur only at the small end of the list and insertions occur only away from the small end. Our analysis leads to a data structure for representing sorted lists when the access pattern exhibits a (perhaps time-varying) locality of reference. This structure has many of the properties of the representation proposed by Guibas, McCreight, Plass, and Roberts [1977], but it is substantially simpler and may be practical for lists of moderate size.

Design and analysis of a data structure for representing sorted lists

Sensitivity analysis of minimum spanning trees and shortest path trees

Isomorphism of Planar Graphs.

Komlos has devised a way to use a linear number of binary comparisons to test whether a given spanning tree of a graph with edge costs is a minimum spanning tree. The total computational work required by his method is much larger than linear, however. This paper describes a linear-time algorithm for verifying a minimum spanning tree. This algorithm combines the result of Komlos with a preprocessing and table look-up method for small subproblems and with a previously known almost-linear-time algorithm. Additionally, an optimal deterministic algorithm and a linear-time randomized algorithm for sensitivity analysis of minimum spanning trees are presented.

Robert E. Tarjan

Papers

A Combinatorial Problem Which Is Complete in Polynomial Space

Design and analysis of a data structure for representing sorted lists

Sensitivity analysis of minimum spanning trees and shortest path trees

Isomorphism of Planar Graphs.

Verification and sensitivity analysis of minimum spanning trees in linear time