Above are papers related to the following paper: [Paper_ID] Paper #13656. 	[Title] Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns [Abstract] This paper introduces and explores some representational biases for efficient learning of spatial, temporal, or spatio-temporal patterns in connectionist networks (CN) massively parallel networks of simple computing elements. It examines learning mechanisms that constructively build up network structures that encode information from environmental stimuli at successively higher resolutions as needed for the tasks (e.g., perceptual recognition) that the network has to perform. Some simple examples are presented to illustrate the the basic structures and processes used in such networks to ensure the parsimony of learned representations by guiding the system to focus its efforts at the minimal adequate resolution. Several extensions of the basic algorithm for efficient learning using multi-resolution representations of spatial, temporal, or spatio-temporal patterns are discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #83746. 	[Title] Experiments with the Cascade-Correlation Algorithm [Abstract] Technical Report # 91-16 July 1991; Revised August 1991 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35. 	[Title] 4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley. [Abstract] To implement a specific application, you should only have to change the file app.c. Section 2 describes the routines in app.c in detail. If you use additional variables for your specific problem, the easiest method of making them available to other program units is to declare them in sga.h and external.h. However, take care that you do not redeclare existing variables. Two example applications files are included in the SGA-C distribution. The file app1.c performs the simple example problem included with the Pascal version; finding the maximum of x 10 , where x is an integer interpretation of a chromosome. A slightly more complex application is include in app2.c. This application illustrates two features that have been added to SGA-C. The first of these is the ithruj2int function, which converts bits i through j in a chromosome to an integer. The second new feature is the utility pointer that is associated with each population member. The example application interprets each chromosome as a set of concatenated integers in binary form. The lengths of these integer fields is determined by the user-specified value of field size, which is read in by the function app data(). The field size must be less than the smallest of the chromosome length and the length of an unsigned integer. An integer array for storing the interpreted form of each chromosome is dynamically allocated and assigned to the chromosome's utility pointer in app malloc(). The ithruj2int routine (see utility.c) is used to translate each chromosome into its associated vector. The fitness for each chromosome is simply the sum of the squares of these integers. This example application will function for any chromosome length. SGA-C is intended to be a simple program for first-time GA experimentation. It is not intended to be definitive in terms of its efficiency or the grace of its implementation. The authors are interested in the comments, criticisms, and bug reports from SGA-C users, so that the code can be refined for easier use in subsequent versions. Please email your comments to rob@galab2.mh.ua.edu, or write to TCGA: The authors gratefully acknowledge support provided by NASA under Grant NGT-50224 and support provided by the National Science Foundation under Grant CTS-8451610. We also thank Hillol Kargupta for donating his tournament selection implementation. Booker, L. B. (1982). Intelligent behavior as an adaptation to the task environment (Doctoral dissertation, Technical Report No. 243. Ann Arbor: University of Michigan, Logic of Computers Group). Dissertations Abstracts International, 43(2), 469B. (University Microfilms No. 8214966) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #119686. 	[Title] Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms [Abstract] Constructive learning algorithms offer an approach to incremental construction of near-minimal artificial neural networks for pattern classification. Examples of such algorithms include Tower, Pyramid, Upstart, and Tiling algorithms which construct multilayer networks of threshold logic units (or, multilayer perceptrons). These algorithms differ in terms of the topology of the networks that they construct which in turn biases the search for a decision boundary that correctly classifies the training set. This paper presents an analysis of such algorithms from a geometrical perspective. This analysis helps in a better characterization of the search bias employed by the different algorithms in relation to the geometrical distribution of examples in the training set. Simple experiments with non linearly separable training sets support the results of mathematical analysis of such algorithms. This suggests the possibility of designing more efficient constructive algorithms that dynamically choose among different biases to build near-minimal networks for pattern classification. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #198443. 	[Title] Properties of Genetic Representations of Neural Architectures [Abstract] Genetic algorithms and related evolutionary techniques offer a promising approach for automatically exploring the design space of neural architectures for artificial intelligence and cognitive modeling. Central to this process of evolutionary design of neural architectures (EDNA) is the choice of the representation scheme that is used to encode a neural architecture in the form of a gene string (genotype) and to decode a genotype into the corresponding neural architecture (phenotype). The representation scheme used not only constrains the class of neural architectures that are representable (evolvable) in the system, but also determines the efficiency and the time-space complexity of the evolutionary design procedure as a whole. This paper identifies and discusses a set of properties that can be used to characterize different representations used in EDNA and to design or select representations with the necessary properties for particular classes of applications. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120431. 	[Title] Symbolic and Subsymbolic Learning for Vision: Some Possibilities [Abstract] Robust, flexible and sufficiently general vision systems such as those for recognition and description of complex 3-dimensional objects require an adequate armamentarium of representations and learning mechanisms. This paper briefly analyzes the strengths and weaknesses of different learning paradigms such as symbol processing systems, connectionist networks, and statistical and syntactic pattern recognition systems as possible candidates for providing such capabilities and points out several promising directions for integrating multiple such paradigms in a synergistic fashion towards that goal. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #80656. 	[Title] Perceptual Development and Learning: From Behavioral, Neurophysiological, and Morphological Evidence To Computational Models [Abstract] An intelligent system has to be capable of adapting to a constantly changing environment. It therefore, ought to be capable of learning from its perceptual interactions with its surroundings. This requires a certain amount of plasticity in its structure. Any attempt to model the perceptual capabilities of a living system or, for that matter, to construct a synthetic system of comparable abilities, must therefore, account for such plasticity through a variety of developmental and learning mechanisms. This paper examines some results from neuroanatomical, morphological, as well as behavioral studies of the development of visual perception; integrates them into a computational framework; and suggests several interesting experiments with computational models that can yield insights into the development of visual perception. In order to understand the development of information processing structures in the brain, one needs knowledge of changes it undergoes from birth to maturity in the context of a normal environment. However, knowledge of its development in aberrant settings is also extremely useful, because it reveals the extent to which the development is a function of environmental experience (as opposed to genetically determined pre-wiring). Accordingly, we consider development of the visual system under both normal and restricted rearing conditions. The role of experience in the early development of the sensory systems in general, and the visual system in particular, has been widely studied through a variety of experiments involving carefully controlled manipulation of the environment presented to an animal. Extensive reviews of such results can be found in (Mitchell, 1984; Movshon, 1981; Hirsch, 1986; Boothe, 1986; Singer, 1986). Some examples of manipulation of visual experience are total pattern deprivation (e.g., dark rearing), selective deprivation of a certain class of patterns (e.g., vertical lines), monocular deprivation in animals with binocular vision, etc. Extensive studies involving behavioral deficits resulting from total visual pattern deprivation indicate that the deficits arise primarily as a result of impairment of visual information processing in the brain. The results of these experiments suggest specific developmental or learning mechanisms that may be operating at various stages of development, and at different levels in the system. We will discuss some of these hhhhhhhhhhhhhhh This is a working draft. All comments, especially constructive criticism and suggestions for improvement, will be appreciated. I am indebted to Prof. James Dannemiller for introducing me to some of the literature in infant development; to Prof. Leonard Uhr for his helpful comments on an initial draft of the paper; and to numerous researchers whose experimental work has provided the basis for the model outlined in this paper. This research was partially supported by grants from the National Science Foundation and the University of Wisconsin Graduate School. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13652. 	[Title] BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN [Abstract] This paper specifies the main features of Brain-like, Neuronal, and Connectionist models; argues for the need for, and usefulness of, appropriate successively larger brain-like structures; and examines parallel-hierarchical Recognition Cone models of perception from this perspective, as examples of such structures. The anatomy, physiology, behavior, and development of the visual system are briefly summarized to motivate the architecture of brain-structured networks for perceptual recognition. Results are presented from simulations of carefully pre-designed Recognition Cone structures that perceive objects (e.g., houses) in digitized photographs. A framework for perceptual learning is introduced, including mechanisms for generation-discovery (feedback-guided growth of new links and nodes, subject to brain-like constraints (e.g., local receptive fields, global convergence-divergence). The information processing transforms discovered through generation are fine-tuned by feedback-guided reweight-ing of links. Some preliminary results are presented of brain-structured networks that learn to recognize simple objects (e.g., letters of the alphabet, cups, apples, bananas) through feedback-guided generation and reweighting. These show large improvements over networks that either lack brain-like structure or/and learn by reweighting of links alone. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #987188. 	[Title] A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers [Abstract] This paper explores some algorithms for automatic quantization of real-valued datasets using thermometer codes for pattern classification applications. Experimental results indicate that a relatively simple randomized thermometer code generation technique can result in quantized datasets that when used to train simple perceptrons, can yield generalization on test data that is substantially better than that obtained with their unquantized counterparts. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13654. 	[Title] Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN) [Abstract] The absence of powerful control structures and processes that synchronize, coordinate, switch between, choose among, regulate, direct, modulate interactions between, and combine distinct yet interdependent modules of large connectionist networks (CN) is probably one of the most important reasons why such networks have not yet succeeded at handling difficult tasks (e.g. complex object recognition and description, complex problem-solving, planning). In this paper we examine how CN built from large numbers of relatively simple neuron-like units can be given the ability to handle problems that in typical multi-computer networks and artificial intelligence programs along with all other types of programs are always handled using extremely elaborate and precisely worked out central control (coordination, synchronization, switching, etc.). We point out the several mechanisms for central control of this un-brain-like sort that CN already have built into them albeit in hidden, often overlooked, ways. We examine the kinds of control mechanisms found in computers, programs, fetal development, cellular function and the immune system, evolution, social organizations, and especially brains, that might be of use in CN. Particularly intriguing suggestions are found in the pacemakers, oscillators, and other local sources of the brain's complex partial synchronies; the diffuse, global effects of slow electrical waves and neurohormones; the developmental program that guides fetal development; communication and coordination within and among living cells; the working of the immune system; the evolutionary processes that operate on large populations of organisms; and the great variety of partially competing partially cooperating controls found in small groups, organizations, and larger societies. All these systems are rich in control but typically control that emerges from complex interactions of many local and diffuse sources. We explore how several different kinds of plausible control mechanisms might be incorporated into CN, and assess their potential benefits with respect to their cost. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1115959. 	[Title] Pruning Strategies for the MTiling Constructive Learning Algorithm [Abstract] We present a framework for incorporating pruning strategies in the MTiling constructive neural network learning algorithm. Pruning involves elimination of redundant elements (connection weights or neurons) from a network and is of considerable practical interest. We describe three elementary sensitivity based strategies for pruning neurons. Experimental results demonstrate a moderate to significant reduction in the network size without compromising the network's generalization performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13686. 	[Title] Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm [Abstract] Technical Report 1123, Computer Sciences Department, University of Wisconsin - Madison, Nov. 1992 ABSTRACT This article describes an approach to combining symbolic and connectionist approaches to machine learning. A three-stage framework is presented and the research of several groups is reviewed with respect to this framework. The first stage involves the insertion of symbolic knowledge into neural networks, the second addresses the refinement of this prior knowledge in its neural representation, while the third concerns the extraction of the refined symbolic knowledge. Experimental results and open research issues are discussed. A shorter version of this paper will appear in Machine Learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13658. 	[Title] Generative Learning Structures and Processes for Generalized Connectionist Networks [Abstract] Massively parallel networks of relatively simple computing elements offer an attractive and versatile framework for exploring a variety of learning structures and processes for intelligent systems. This paper briefly summarizes some popular learning structures and processes used in such networks. It outlines a range of potentially more powerful alternatives for pattern-directed inductive learning in such systems. It motivates and develops a class of new learning algorithms for massively parallel networks of simple computing elements. We call this class of learning processes generative for they offer a set of mechanisms for constructive and adaptive determination of the network architecture the number of processing elements and the connectivity among them as a function of experience. Generative learning algorithms attempt to overcome some of the limitations of some approaches to learning in networks that rely on modification of weights on the links within an otherwise fixed network topology e.g., rather slow learning and the need for an a-priori choice of a network architecture. Several alternative designs as well as a range of control structures and processes which can be used to regulate the form and content of internal representations learned by such networks are examined. Empirical results from the study of some generative learning algorithms are briefly summarized and several extensions and refinements of such algorithms, and directions for future research are outlined. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #294239. 	[Title] Faster Learning in Multi-Layer Networks by Handling [Abstract] Generalized delta rule, popularly known as back-propagation (BP) [9, 5] is probably one of the most widely used procedures for training multi-layer feed-forward networks of sigmoid units. Despite reports of success on a number of interesting problems, BP can be excruciatingly slow in converging on a set of weights that meet the desired error criterion. Several modifications for improving the learning speed have been proposed in the literature [2, 4, 8, 1, 6]. BP is known to suffer from the phenomenon of flat spots [2]. The slowness of BP is a direct consequence of these flat-spots together with the formulation of the BP Learning rule. This paper proposes a new approach to minimizing the error that is suggested by the mathematical properties of the conventional error function and that effectively handles flat-spots occurring in the output layer. The robustness of the proposed technique is demonstrated on a number of data-sets widely studied in the machine learning community. Please summarise all the given information.

Paper #13656 has the title `Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns  
` and the abstract `This paper introduces and explores some representational biases for efficient learning of spatial, temporal, or spatio-temporal patterns in connectionist networks (CN) massively parallel networks of simple computing elements. It examines learning mechanisms that constructively build up network structures that encode information from environmental stimuli at successively higher resolutions as needed for the tasks (e.g., perceptual recognition) that the network has to perform. Some simple examples are presented to illustrate the the basic structures and processes used in such networks to ensure the parsimony of learned representations by guiding the system to focus its efforts at the minimal adequate resolution. Several extensions of the basic algorithm for efficient learning using multi-resolution representations of spatial, temporal, or spatio-temporal patterns are discussed. 
`.
Question: Which category should Paper #13656 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #13656 has the title `Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns  
` and the abstract `This paper introduces and explores some representational biases for efficient learning of spatial, temporal, or spatio-temporal patterns in connectionist networks (CN) massively parallel networks of simple computing elements. It examines learning mechanisms that constructively build up network structures that encode information from environmental stimuli at successively higher resolutions as needed for the tasks (e.g., perceptual recognition) that the network has to perform. Some simple examples are presented to illustrate the the basic structures and processes used in such networks to ensure the parsimony of learned representations by guiding the system to focus its efforts at the minimal adequate resolution. Several extensions of the basic algorithm for efficient learning using multi-resolution representations of spatial, temporal, or spatio-temporal patterns are discussed. 
`.
Question: Which category should Paper #13656 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks