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Articles 781 - 810 of 823
Full-Text Articles in Computer Sciences
Breakpoint Skeletal Representation And Compression Of Document Images, William A. Barrett, Bryan S. Morse, Eric N. Mortensen
Breakpoint Skeletal Representation And Compression Of Document Images, William A. Barrett, Bryan S. Morse, Eric N. Mortensen
Faculty Publications
We present a new method for representation and (lossy) compression of bitonal document images. The technique extracts a skeletal medial axis from each object using a true Euclidean distance map of the image and then finds piecewise linear breakpoints in the skeleton to create a breakpoint skeletal representation, bps, (Fig. 1). The bps is encoded for each object as a set of triples {, <Δx2,Δy2,Δr2>, . . . <Δxn,Δyn,Δrn>} where contains the coordinate and distance (radius, r1) of the initial breakpoint from the closest point on the perimeter of the object and <Δxi,Δyi,Δri> represents the difference in location and radius between …
Bias And The Probability Of Generalization, Tony R. Martinez, D. Randall Wilson
Bias And The Probability Of Generalization, Tony R. Martinez, D. Randall Wilson
Faculty Publications
In order to be useful, a learning algorithm must be able to generalize well when faced with inputs not previously presented to the system. A bias is necessary for any generalization, and as shown by several researchers in recent years, no bias can lead to strictly better generalization than any other when summed over all possible functions or applications. This paper provides examples to illustrate this fact, but also explains how a bias or learning algorithm can be “better” than another in practice when the probability of the occurrence of functions is taken into account. It shows how domain knowledge …
Improved Heterogeneous Distance Functions, Tony R. Martinez, D. Randall Wilson
Improved Heterogeneous Distance Functions, Tony R. Martinez, D. Randall Wilson
Faculty Publications
Instance-based learning techniques typically handle continuous and linear input values well, but often do not handle nominal input attributes appropriately. The Value Difference Metric (VDM) was designed to find reasonable distance values between nominal attribute values, but it largely ignores continuous attributes, requiring discretization to map continuous values into nominal values. This paper proposes three new heterogeneous distance functions, called the Heterogeneous Value Difference Metric (HVDM), the Interpolated Value Difference Metric (IVDM), and the Windowed Value Difference Metric (WVDM). These new distance functions are designed to handle applications with nominal attributes, continuous attributes, or both. In experiments on 48 applications …
Faster Ray Tracing Using Adaptive Grids, Thomas W. Sederberg, Krysztof S. Klimaszewski
Faster Ray Tracing Using Adaptive Grids, Thomas W. Sederberg, Krysztof S. Klimaszewski
Faculty Publications
A new hybrid approach is presented which outperforms the regular grid technique in scenes with highly irregular object distributions by a factor of hundreds, and combined with an area interpolator, by a factor of thousands. Much has been said about scene independence of different acceleration techniques and the alleged superiority of one approach over another. Several theoretical and practical studies conducted in the past have led to the same conclusion: a space partitioning method that allows the fastest rendering of one scene often fails with another. Specialization may be the answer. This has always been pursued, consciously or not, in …
A Fertility Channel Model For Post-Correction Of Continuous Speech Recognition, Eric K. Ringger, James F. Allen
A Fertility Channel Model For Post-Correction Of Continuous Speech Recognition, Eric K. Ringger, James F. Allen
Faculty Publications
We have implemented a post-processor called SPEECHPP to correct word-level errors committed by an arbitrary speech recognizer. Applying a noisy-channel model, SPEECHPP uses a Viterbi beam-search that employs language and channel models. Previous work demonstrated that a simple word-for-word channel model was sufficient to yield substantial incieases in word accuracy. This paper demonstrates that some improvements in word accuracy result from augmenting the channel model with an account of word fertility in the channel. This work further demonstrates that a modern continuous speech recognizer can be used in "black-box" fashion for robustly recognizing speech for which the recognizer was not …
Procedurally Rational Decision-Making And Control, Richard L. Frost, Michael A. Goodrich, Wynn C. Stirling
Procedurally Rational Decision-Making And Control, Richard L. Frost, Michael A. Goodrich, Wynn C. Stirling
Faculty Publications
Substantive rationality requires a decision-maker to be a utility maximizer; under this paradigm, the decision is paramount, and not dependent on the computational process used to obtain it. Procedural rationality is dependent on the method used to make the decision; reasonableness of the procedure is paramount. Well-formed problems are amenable to substantive rationality; ill-formed problems are not, but are amenable to procedural rationality. To qualify as being procedurally rational, a methodology must possess a sound epistemological basis, it must be amenable to a formal design synthesis procedure, and it must be consistent with substantive rationality. Epistemic utility theory forms the …
Robust Optimization Using Training Set Evolution, Tony R. Martinez, Dan A. Ventura
Robust Optimization Using Training Set Evolution, Tony R. Martinez, Dan A. Ventura
Faculty Publications
Training Set Evolution is an eclectic optimization technique that combines evolutionary computation (EC) with neural networks (NN). The synthesis of EC with NN provides both initial unsupervised random exploration of the solution space as well as supervised generalization on those initial solutions. An assimilation of a large amount of data obtained over many simulations provides encouraging empirical evidence for the robustness of Evolutionary Training Sets as an optimization technique for feedback and control problems.
A Robust System For Natural Spoken Dialogue, Eric K. Ringger, James F. Allen, Bradford W. Miller, Teresa Sikorski
A Robust System For Natural Spoken Dialogue, Eric K. Ringger, James F. Allen, Bradford W. Miller, Teresa Sikorski
Faculty Publications
This paper describes a system that leads us to believe in the feasibility of constructing natural spoken dialogue systems in task-oriented domains. It specifically addresses the issue of robust interpretation of speech in the presence of recognition errors. Robustness is achieved by a combination of statistical error post-correction, syntactically- and semantically-driven robust parsing, and extensive use of the dialogue context. We present an evaluation of the system using time-to-completion and the quality of the final solution that suggests that most native speakers of English can use the system successfully with virtually no training.
Heterogeneous Radial Basis Function Networks, Tony R. Martinez, D. Randall Wilson
Heterogeneous Radial Basis Function Networks, Tony R. Martinez, D. Randall Wilson
Faculty Publications
Radial Basis Function (RBF) networks typically use a distance function designed for numeric attributes, such as Euclidean or city-block distance. This paper presents a heterogeneous distance function which is appropriate for applications with symbolic attributes, numeric attributes, or both. Empirical results on 30 data sets indicate that the heterogeneous distance metric yields significantly improved generalization accuracy over Euclidean distance in most cases involving symbolic attributes.
A Brief Introduction To Formal Methods, Paul E. Black, Kelly M. Hall, Michael D. Jones, Trent N. Larson, Phillip J. Windley
A Brief Introduction To Formal Methods, Paul E. Black, Kelly M. Hall, Michael D. Jones, Trent N. Larson, Phillip J. Windley
Faculty Publications
As hardware designs grow in size and complexity, current design methods are proving less adequate. Current methods for specification, design, and test are typically empirical or informal, that is, they are based on experience and argument. Formal methods are solidly based on mathematical logic systems and precise rules of inference. Formal methods offer a discipline which complements current methods so designers can successfully meet the demand for high performance systems. Formal methods covers a broad and diverse set of techniques aimed at improving computer correctness. This paper explains the role of specifications and implementation models in formal methods, and different …
Error Correction Via A Post-Processor For Continuous Speech Recognition, Eric K. Ringger, James F. Allen
Error Correction Via A Post-Processor For Continuous Speech Recognition, Eric K. Ringger, James F. Allen
Faculty Publications
This paper presents a new technique for overcoming several types of speech recognition errors by post-processing the output of a continuous speech recognizer. The post-processor output contains fewer errors, thereby making interpretation by higher-level modules, such as a parser, in a speech understanding system more reliable. The primary advantage to the post-processing approach over existing approaches for overcoming SR errors lies in its abilityto introduce options that are not available in the SR module’s output. This work provides evidence for the claim that a modern continuous speech recognizer can be used successfully in “black-box” fashion for robustly interpreting spontaneous utterances …
Compressing Semi-Structured Text Using Hierarchical Phrase Identifications, Dan R. Olsen Jr., Craig G. Nevill-Manning, Ian H. Witten
Compressing Semi-Structured Text Using Hierarchical Phrase Identifications, Dan R. Olsen Jr., Craig G. Nevill-Manning, Ian H. Witten
Faculty Publications
The structure of this paper is as follows. We begin by identifying some characteristics of semi-structured text that have special relevance to data compression. We then give a brief account of a particular large textual database, and describe a compression scheme that exploits its structure. In addition to providing compression, the system gives some insight into the structure of the database. Finally we show how the hierarchical grammar can be generalized, first manually and then automatically, to yield further improvements in compression performance.
A Provably Convergent Dynamic Training Method For Multi-Layer Perceptron Networks, Timothy L. Andersen, Tony R. Martinez
A Provably Convergent Dynamic Training Method For Multi-Layer Perceptron Networks, Timothy L. Andersen, Tony R. Martinez
Faculty Publications
This paper presents a new method for training multi-layer perceptron networks called DMP1 (Dynamic Multilayer Perceptron 1). The method is based upon a divide and conquer approach which builds networks in the form of binary trees, dynamically allocating nodes and layers as needed. The individual nodes of the network are trained using a genetic algorithm. The method is capable of handling real-valued inputs and a proof is given concerning its convergence properties of the basic model. Simulation results show that DMP1 performs favorably in comparison with other learning algorithms.
An Integrated Framework For Learning And Reasoning, Christophe G. Giraud-Carrier, Tony R. Martinez
An Integrated Framework For Learning And Reasoning, Christophe G. Giraud-Carrier, Tony R. Martinez
Faculty Publications
Learning and reasoning are both aspects of what is considered to be intelligence. Their studies within AI have been separated historically, learning being the topic of machine learning and neural networks, and reasoning falling under classical (or symbolic) AI. However, learning and reasoning are in many ways interdependent. This paper discusses the nature of some of these interdependencies and proposes a general framework called FLARE, that combines inductive learning using prior knowledge together with reasoning in a propositional setting. Several examples that test the framework are presented, including classical induction, many important reasoning protocols and two simple expert systems.
Surface Intersection Loop Destruction, Thomas W. Sederberg, Alan K. Zundel
Surface Intersection Loop Destruction, Thomas W. Sederberg, Alan K. Zundel
Faculty Publications
The intersection curve between two surface patches consists of one or more connected components or branches. Each component can be classified as either an open branch, with endpoints on at least one patch boundary, or as a closed loop.
Hodographs And Normals Of Rational Curves And Surfaces, Thomas W. Sederberg, Takafumi Saito, Guo-Jin Wang
Hodographs And Normals Of Rational Curves And Surfaces, Thomas W. Sederberg, Takafumi Saito, Guo-Jin Wang
Faculty Publications
Derivatives and normals of rational Bézier curves and surface patches are discussed. A non-uniformly scaled hodograph of a degree m x n tensor-product rational surface, which provides correct derivative direction but not magnitude, can be written as a degree (2m - 2) x 2n or 2m x (2n - 2) vector function in polynomial Bézier form. Likewise, the scaled normal direction is degree (3m - 2) x(3n - 2). Efficient methods are developed for bounding these directions and the derivative magnitude.
Using Multiple Statistical Prototypes To Classify Continuously Valued Data, Tony R. Martinez, Dan A. Ventura
Using Multiple Statistical Prototypes To Classify Continuously Valued Data, Tony R. Martinez, Dan A. Ventura
Faculty Publications
Multiple Statistical Prototypes (MSP) is a modification of a standard minimum distance classification scheme that generates muItiple prototypes per class using a modified greedy heuristic. Empirical comparison of MSP with other well-known learning algorithms shows MSP to be a robust algorithm that uses a very simple premise to produce good generalization and achieve parsimonious hypothesis representation.
A Vlsi Implementation Of A Parallel, Self-Organizing Learning Model, Tony R. Martinez, George L. Rudolph, Linton G. Salmon, Matthew G. Stout
A Vlsi Implementation Of A Parallel, Self-Organizing Learning Model, Tony R. Martinez, George L. Rudolph, Linton G. Salmon, Matthew G. Stout
Faculty Publications
This paper presents a VLSI implementation of the Priority Adaptive Self-organizing Concurrent System (PASOCS) learning model that is built using a multi-chip module (MCM) substrate. Many current hardware implementations of neural network learning models are direct implementations of classical neural network structures - a large number of sample computing nodes connected by a dense number of weighted links. PASOCS is one of a class of ASOCS (Adaptive Self-Organizing Concurrent System) connectionist models whose overall goal is the same as classical neural networks models, but whose functional mechanisms differ significantly. This model has potential application in areas such as pattern recognition, …
Spiders: A New User Interface For Rotation And Visualization Of N-Dimensional Point Sets, William A. Barrett, Kirk L. Duffin
Spiders: A New User Interface For Rotation And Visualization Of N-Dimensional Point Sets, William A. Barrett, Kirk L. Duffin
Faculty Publications
We present a new method for creating n-dimensional rotation matrices from manipulating the projections of n-dimensional data coordinate axes onto a viewing plane. A user interface for n-dimensional rotation is implemented. The interface is shown to have no rotational hysteresis.
A Multi-Chip Module Implementation Of A Neural Network, Tony R. Martinez, George L. Rudolph, Linton G. Salmon, Matthew G. Stout
A Multi-Chip Module Implementation Of A Neural Network, Tony R. Martinez, George L. Rudolph, Linton G. Salmon, Matthew G. Stout
Faculty Publications
The requirement for dense interconnect in artificial neural network systems has led researchers to seek high-density interconnect technologies. This paper reports an implementation using multi-chip modules (MCMs) as the interconnect medium. The specific system described is a self-organizing, parallel, and dynamic learning model which requires a dense interconnect technology for effective implementation; this requirement is fulfilled by exploiting MCM technology. The ideas presented in this paper regarding an MCM implementation of artificial neural networks are versatile and can be adapted to apply to other neural network and connectionist models.
Proof Of Correctness For Asocs Aa3 Networks, J. Cory Barker, Tony R. Martinez
Proof Of Correctness For Asocs Aa3 Networks, J. Cory Barker, Tony R. Martinez
Faculty Publications
This paper analyzes adaptive algorithm 3 (AA3) of adaptive self-organizing concurrent systems (ASOCS) and proves that AA3 correctly fulfills the rules presented. Several different models for ASOCS have been developed. AA3 uses a distributed mechanism for implementing rules so correctness is not obvious. An ASOCS is an adaptive network composed of many simple computing elements operating in parallel. An ASOCS operates in one of two modes: learning and processing. In learning mode, rules are presented to the ASOCS and incorporated in a self-organizing fashion. In processing mode, the ASOCS acts as a parallel hardware circuit that performs the function defined …
Towards A General Distributed Platform For Learning And Generalization, Brent W. Hughes, Tony R. Martinez
Towards A General Distributed Platform For Learning And Generalization, Brent W. Hughes, Tony R. Martinez
Faculty Publications
Different learning models employ different styles of generalization on novel inputs. This paper proposes the need for multiple styles of generalization to support a broad application base. The Priority ASOCS model (Priority Adaptive Self-organizing Concurrent System) is overviewed and presented as a potential platform which can support multiple generalization styles. PASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. The PASOCS can operate in either a data processing mode or a learning mode. During data processing mode, the system acts as a parallel hardware circuit. During leaming mode, the PASOCS incorporates rules, with …
The Importance Of Using Multiple Styles Of Generalization, Tony R. Martinez, D. Randall Wilson
The Importance Of Using Multiple Styles Of Generalization, Tony R. Martinez, D. Randall Wilson
Faculty Publications
There are many ways for a learning system to generalize from training set data. There is likely no one style of generalization which will solve all problems better than any other style, for different styles will work better on some applications than others. This paper presents several styles of generalization and uses them to suggest that a collection of such styles can provide more accurate generalization than any one style by itself. Empirical results of generalizing on several real-world applications are given, and comparisons are made on the generalization accuracy of each style of generalization. The empirical results support the …
Rsvp: A New Resource Reservation Protocol, Daniel Zappala, Stephen Deering, Deborah Estrin, Scott Shenker, Lixia Zhang
Rsvp: A New Resource Reservation Protocol, Daniel Zappala, Stephen Deering, Deborah Estrin, Scott Shenker, Lixia Zhang
Faculty Publications
The current Internet architecture, embodied in the Internet Protocol (IP) network protocol, offers a very simple service model: point-to-point best-effort service. In recent years, several new classes of distributed applications have been developed, such as remote video, multimedia conferencing, data fusion, visualization, and virtual reality. It is becoming increasingly clear that the Internet’s primitive service model is inadequate for these new applications. This inadequacy stems from the failure of the point-to-point best-effort service model to address two application requirements. First, many of these applications are very sensitive to the quality of service their packets receive. For a network to deliver …
Adaptive Boundary Detection Using “Live-Wire” Two-Dimensional Dynamic Programming, William A. Barrett, Bryan S. Morse, Eric N. Mortensen, Jayaram Udupa
Adaptive Boundary Detection Using “Live-Wire” Two-Dimensional Dynamic Programming, William A. Barrett, Bryan S. Morse, Eric N. Mortensen, Jayaram Udupa
Faculty Publications
An adaptive boundary detection algorithm that uses two-dimensional dynamic programming is presented. The algorithm is less constrained than previous one-dimensional dynamic programming algorithms and allows the user to interactively determine the mathematically optimal boundary between a user-selected seed point and any other dynamically selected "free” point in the image. Interactive movement of the free point by the cursor causes the boundary to behave like a “live wire” as it adapts to the new minimum cost path between the seed point and the currently selected free point. The algorithm can also be adapted or customized to learn boundary-defining features for a …
Approximation By Interval Bezier Curves, Thomas W. Sederberg, Rida T. Farouki
Approximation By Interval Bezier Curves, Thomas W. Sederberg, Rida T. Farouki
Faculty Publications
The interval Bezier curve, which, unlike other curve and surface approximation schemes, can transfer a complete description of approximation errors between diverse CAD/CAM systems that impose fundamentally incompatible constraints on their canonical representation schemes, is described. Interval arithmetic, which offers an essentially infallible way to monitor error propagation in numerical algorithms that use floating-point arithmetic is reviewed. Affine maps, the computations of which are key operations in the de Casteljau subdivision and degree-elevation algorithms for Bezier curves, the floating-point error propagation in such computations, approximation by interval polynomials, and approximation by interval Bezier curves are discussed.
A Self-Organizing Binary Decision Tree For Incrementally Defined Rule-Based Systems, Douglas M. Campbell, Tony R. Martinez
A Self-Organizing Binary Decision Tree For Incrementally Defined Rule-Based Systems, Douglas M. Campbell, Tony R. Martinez
Faculty Publications
This paper presents an adaptive self-organizing concurrent system (ASOCS) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. This paper focuses on adaptive algorithm 3 (AA3) and details its architecture and learning algorithm. It has advantages over previous ASOCS models in simplicity, implementability, and cost. An ASOCS can operate in either a data processing mode or a learning mode. During the data processing mode, an ASOCS acts as a parallel …
Techniques For Cubic Algebraic Surfaces Ii, Thomas W. Sederberg
Techniques For Cubic Algebraic Surfaces Ii, Thomas W. Sederberg
Faculty Publications
A survey of some techniques that may have potential for free-form modeling with algebraic surfaces is continued. Classical results as well as several recent innovations are included. Specific attention is paid to cubic algebraic surfaces, although many of the ideas presented have application to algebraic surfaces of any degree. Topics addressed include piecewise constructions, interpolation to points and space curves, and parameterization.
Techniques For Cubic Algebraic Surfaces I, Thomas W. Sederberg
Techniques For Cubic Algebraic Surfaces I, Thomas W. Sederberg
Faculty Publications
The tutorial presents some tools for free-form modeling with algebraic surfaces, that is, surfaces that can be defined using an implicit polynomial equation f(x, y, z )=0. Cubic algebraic surfaces (defined by an implicit equation of degree 3) are emphasized. While much of this material applies only to cubic surfaces, some applies to algebraic surfaces of any degree. This area of the tutorial introduces terminology, presents different methods for defining and modeling with cubic surfaces, and examines the power basis representation of algebraic surfaces. Methods of forcing an algebraic surface to interpolate a set of points or a space curve …
Consistency And Generalization In Incrementally Trained Connectionist Networks, Tony R. Martinez
Consistency And Generalization In Incrementally Trained Connectionist Networks, Tony R. Martinez
Faculty Publications
This paper discusses aspects of consistency and generalization in connectionist networks which learn through incremental training by examples or rules. Differences between training set learning and incremental rule or example learning are presented. Generalization, the ability to output reasonable mappings when presented with novel input patterns, is discussed in light of the above learning methods. In particular, the contrast between humming distance generalization and generalizing by high order combinations of critical variables is overviewed. Examples of detailed rules for an incremental learning model are presented for both consistency and generalization constraints.