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Parallel Coupled Micro-Macro Actuators
(1996-01-01)
This thesis presents a new actuator system consisting of a micro-actuator and a macro-actuator coupled in parallel via a compliant transmission. The system is called the Parallel Coupled Micro-Macro Actuator, or PaCMMA. ...
The Informational Complexity of Learning from Examples
(1996-09-01)
This thesis attempts to quantify the amount of information needed to learn certain tasks. The tasks chosen vary from learning functions in a Sobolev space using radial basis function networks to learning grammars in ...
A Formulation for Active Learning with Applications to Object Detection
(1996-06-06)
We discuss a formulation for active example selection for function learning problems. This formulation is obtained by adapting Fedorov's optimal experiment design to the learning problem. We specifically show how to ...
A Security Kernel Based on the Lambda-Calculus
(1996-03-13)
Cooperation between independent agents depends upon establishing adegree of security. Each of the cooperating agents needs assurance that the cooperation will not endanger resources of value to that agent. In a computer ...
Edge and Mean Based Image Compression
(1996-11-01)
In this paper, we present a static image compression algorithm for very low bit rate applications. The algorithm reduces spatial redundancy present in images by extracting and encoding edge and mean information. Since ...
Virtual Model Control of a Hexapod Walking Robot
(1996-12-01)
Since robots are typically designed with an individual actuator at each joint, the control of these systems is often difficult and non-intuitive. This thesis explains a more intuitive control scheme called Virtual Model ...
Probabilistic Independence Networks for Hidden Markov Probability Models
(1996-03-13)
Graphical techniques for modeling the dependencies of randomvariables have been explored in a variety of different areas includingstatistics, statistical physics, artificial intelligence, speech recognition, image ...
Factorial Hidden Markov Models
(1996-02-09)
We present a framework for learning in hidden Markov models with distributed state representations. Within this framework, we derive a learning algorithm based on the Expectation--Maximization (EM) procedure for maximum ...
Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks
(1996-02-09)
Sigmoid type belief networks, a class of probabilistic neural networks, provide a natural framework for compactly representing probabilistic information in a variety of unsupervised and supervised learning problems. ...
Image Based Rendering Using Algebraic Techniques
(1996-11-01)
This paper presents an image-based rendering system using algebraic relations between different views of an object. The system uses pictures of an object taken from known positions. Given three such images it can ...