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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 15:24:28 +0000Incremental Rule-based Learning
https://resolver.caltech.edu/CaltechAUTHORS:20190314-142001533
Authors: {'items': [{'id': 'Higgins-C-M', 'name': {'family': 'Higgins', 'given': 'Charles M.'}}, {'id': 'Goodman-R-M', 'name': {'family': 'Goodman', 'given': 'Rodney M.'}}]}
Year: 1991
DOI: 10.1109/isit.1991.695344
In a system which learns to predict the value of an output variable given one or more input variables by looking at a set of examples, a rule-based knowledge representation provides not only a natural method of constructing a classifier, but also a human-readable explanation of what has been learned. Consider a rule of the form if y then x where y is a conjunction of values of input variables and x is a value of the output variable. The number of input variables in y is called the order of the rule. In previous work, a measure of the information content or "value" of such a rule has been developed (the J-measure. It has been shown in [3] that a classifier can be built from the rules obtained by a constrained search of all possible rules which performs comparably with other classifiers.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/paqsj-wzc27Incremental learning with rule-based neural networks
https://resolver.caltech.edu/CaltechAUTHORS:20190314-142000764
Authors: {'items': [{'id': 'Higgins-C-M', 'name': {'family': 'Higgins', 'given': 'C. M.'}}, {'id': 'Goodman-R-M', 'name': {'family': 'Goodman', 'given': 'R. M.'}}]}
Year: 1991
DOI: 10.1109/ijcnn.1991.155294
A classifier for discrete-valued variable classification problems is presented. The system utilizes an information-theoretic algorithm for constructing informative rules from example data. These rules are then used to construct a neural network to perform parallel inference and posterior probability estimation. The network can be grown incrementally, so that new data can be incorporated without repeating the training on previous data. It is shown that this technique performs as well as other techniques such as backpropagation while having unique advantages in incremental learning capability, training efficiency, knowledge representation, and hardware implementation suitability.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/11yy9-8df91Learning fuzzy rule-based neural networks for function approximation
https://resolver.caltech.edu/CaltechAUTHORS:20190314-155127145
Authors: {'items': [{'id': 'Higgins-C-M', 'name': {'family': 'Higgins', 'given': 'C. M.'}}, {'id': 'Goodman-R-M', 'name': {'family': 'Goodman', 'given': 'R. M.'}}]}
Year: 1992
DOI: 10.1109/ijcnn.1992.287127
In this paper, we present a method for the induction of fuzzy logic rules to predict a numerical function from samples of the function and its dependent variables. This method uses an information-theoretic approach based on our previous work with discrete-valued data [3]. The rules learned can then be used in a neural network to predict the function value based upon its dependent variables. An example is shown of learning a control system function.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/4kqn3-31f69Rule-based neural networks for classification and probability estimation
https://resolver.caltech.edu/CaltechAUTHORS:GOOnc92
Authors: {'items': [{'id': 'Goodman-R-M', 'name': {'family': 'Goodman', 'given': 'Rodney M.'}}, {'id': 'Higgins-C-M', 'name': {'family': 'Higgins', 'given': 'Charles M.'}}, {'id': 'Miller-J-W', 'name': {'family': 'Miller', 'given': 'John W.'}}, {'id': 'Smyth-P', 'name': {'family': 'Smyth', 'given': 'Padhraic'}}]}
Year: 1992
DOI: 10.1162/neco.1992.4.6.781
In this paper we propose a network architecture that combines a rule-based approach with that of the neural network paradigm. Our primary motivation for this is to ensure that the knowledge embodied in the network is explicitly encoded in the form of understandable rules. This enables the network's decision to be understood, and provides an audit trail of how that decision was arrived at. We utilize an information theoretic approach to learning a model of the domain knowledge from examples. This model takes the form of a set of probabilistic conjunctive rules between discrete input evidence variables and output class variables. These rules are then mapped onto the weights and nodes of a feedforward neural network resulting in a directly specified architecture. The network acts as parallel Bayesian classifier, but more importantly, can also output posterior probability estimates of the class variables. Empirical tests on a number of data sets show that the rule-based classifier performs comparably with standard neural network classifiers, while possessing unique advantages in terms of knowledge representation and probability estimation.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/w6w9f-eaa23Learning Fuzzy Rule-Based Neural Networks for Control
https://resolver.caltech.edu/CaltechAUTHORS:20160203-163952250
Authors: {'items': [{'id': 'Higgins-C-M', 'name': {'family': 'Higgins', 'given': 'Charles M.'}}, {'id': 'Goodman-R-M', 'name': {'family': 'Goodman', 'given': 'Rodney M.'}}]}
Year: 1993
A three-step method for function approximation with a fuzzy system is proposed. First, the membership functions and an initial rule representation are learned; second, the rules are compressed as much as possible using information theory; and finally, a computational network is constructed to compute the function value. This system is applied to two control examples: learning the truck and trailer backer-upper control system, and learning a cruise control
system for a radio-controlled model car.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/4rfnf-ejn40Fuzzy rule-based networks for control
https://resolver.caltech.edu/CaltechAUTHORS:20190315-142400048
Authors: {'items': [{'id': 'Higgins-C-M', 'name': {'family': 'Higgins', 'given': 'Charles M.'}}, {'id': 'Goodman-R-M', 'name': {'family': 'Goodman', 'given': 'Rodney M.'}}]}
Year: 1994
DOI: 10.1109/91.273129
We present a method for learning fuzzy logic membership functions and rules to approximate a numerical function from a set of examples of the function's independent variables and the resulting function value. This method uses a three-step approach to building a complete function approximation system: first, learning the membership functions and creating a cell-based rule representation; second, simplifying the cell-based rules using an information-theoretic approach for induction of rules from discrete-valued data; and, finally, constructing a computational (neural) network to compute the function value given its independent variables. This function approximation system is demonstrated with a simple control example: learning the truck and trailer backer-upper control system.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/ad2ma-5wg55An Integrated Vision Sensor for the Computation of Optical Flow Singular Points
https://resolver.caltech.edu/CaltechAUTHORS:20160229-160500915
Authors: {'items': [{'id': 'Higgins-C-M', 'name': {'family': 'Higgins', 'given': 'Charles M.'}}, {'id': 'Koch-C', 'name': {'family': 'Koch', 'given': 'Christof'}, 'orcid': '0000-0001-6482-8067'}]}
Year: 1999
A robust, integrative algorithm is presented for computing the position of the focus of expansion or axis of rotation (the singular point) in optical flow fields such as those generated by self-motion. Measurements are shown of a fully parallel CMOS analog VLSI motion sensor array which
computes the direction of local motion (sign of optical flow) at each pixel and can directly implement this algorithm. The flow field singular point is computed in real time with a power consumption of less than 2 mW.
Computation of the singular point for more general flow fields requires measures of field expansion and rotation, which it is shown can also be computed in real-time hardware, again using only the sign of the optical
flow field. These measures, along with the location of the singular point, provide robust real-time self-motion information for the visual guidance of a moving platform such as a robot.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/1gb87-beh35A Modular Multi-Chip Neuromorphic Architecture for Real-Time Visual Motion Processing
https://resolver.caltech.edu/CaltechAUTHORS:20130816-103146617
Authors: {'items': [{'id': 'Higgins-C-M', 'name': {'family': 'Higgins', 'given': 'Charles M.'}}, {'id': 'Koch-C', 'name': {'family': 'Koch', 'given': 'Christof'}, 'orcid': '0000-0001-6482-8067'}]}
Year: 2000
DOI: 10.1023/A:1008309524326
The extent of pixel-parallel focal plane image processing is limited by pixel area and imager fill factor. In this paper, we describe a novel multi-chip neuromorphic VLSI visual motion processing system which combines analog circuitry with an asynchronous digital interchip communications protocol to allow more complex pixel-parallel motion processing than is possible in the focal plane. This multi-chip system retains the primary advantages of focal plane neuromorphic image processors: low-power consumption, continuous-time operation, and small size. The two basic VLSI building blocks are a photosensitive sender chip which incorporates a 2D imager array and transmits the position of moving spatial edges, and a receiver chip which computes a 2D optical flow vector field from the edge information. The elementary two-chip motion processing system consisting of a single sender and receiver is first characterized. Subsequently, two three-chip motion processing systems are described. The first three-chip system uses two sender chips to compute the presence of motion only at a particular stereoscopic depth from the imagers. The second three-chip system uses two receivers to simultaneously compute a linear and polar topographic mapping of the image plane, resulting in information about image translation, rotation, and expansion. These three-chip systems demonstrate the modularity and flexibility of the multi-chip neuromorphic approach.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/8dpek-e8146