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  • 1
    Digitale Medien
    Digitale Medien
    Springer
    Algorithmica 1 (1986), S. 17-29 
    ISSN: 1432-0541
    Schlagwort(e): Hashing algorithms ; Complexity ; Lazy deletion strategies ; Birth-death process ; Laplace's method
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Mathematik
    Notizen: Abstract We examine a version of the dynamic dictionary problem in which stored items have expiration times and can be removed from the dictionary once they have expired. We show that under several reasonable assumptions about the distribution of the items, hashing with lazy deletion uses little more space than methods that use eager deletion. The simple algorithm suggested by this observation was used in a program for analyzing integrated circuit artwork.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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  • 2
    Digitale Medien
    Digitale Medien
    Springer
    Algorithmica 1 (1986), S. 31-48 
    ISSN: 1432-0541
    Schlagwort(e): Analysis of Algorithms ; Graph algorithm ; Shortest path ; Euclidean ; Heuristic ; Dijkstra's algorithm ; Priority queue
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Mathematik
    Notizen: Abstract We analyze a simple method for finding shortest paths inEuclidean graphs (where vertices are points in a Euclidean space and edge weights are Euclidean distances between points). For many graph models, the average running time of the algorithm to find the shortest path between a specified pair of vertices in a graph withV vertices andE edges is shown to beO(V) as compared withO(E +V logV) required by the classical algorithm due to Dijkstra.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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  • 3
    Digitale Medien
    Digitale Medien
    Springer
    Algorithmica 3 (1988), S. 1-3 
    ISSN: 1432-0541
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Mathematik
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 4
    Digitale Medien
    Digitale Medien
    Springer
    Algorithmica 3 (1988), S. 289-291 
    ISSN: 1432-0541
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Mathematik
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 5
    Digitale Medien
    Digitale Medien
    Springer
    Algorithmica 6 (1991), S. 597-619 
    ISSN: 1432-0541
    Schlagwort(e): Queues ; Maximum ; Hashing with lazy deletion ; Data structures ; File histories ; Stacks ; Priority queues ; Linear lists ; Symbol tables ; Continued fractions ; Orthogonal polynomials ; Birth-and-death process ; M/M/∞ ; M/G/∞ ; Transforms
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Mathematik
    Notizen: Abstract We answer questions about the distribution of the maximum size of queues and data structures as a function of time. The concept of “maximum” occurs in many issues of resource allocation. We consider several models of growth, including general birth-and-death processes, the M/G/∞ model, and a non-Markovian process (data structure) for processing plane-sweep information in computational geometry, called “hashing with lazy deletion” (HwLD). It has been shown that HwLD is optimal in terms of expected time and dynamic space; our results show that it is also optimal in terms of expectedpreallocated space, up to a constant factor. We take two independent and complementary approaches: first, in Section 2, we use a variety of algebraic and analytical techniques to derive exact formulas for the distribution of the maximum queue size in stationary birth-and-death processes and in a nonstationary model related to file histories. The formulas allow numerical evaluation and some asymptotics. In our second approach, in Section 3, we consider the M/G/∞ model (which includes M/M/∞ as a special case) and use techniques from the analysis of algorithms to get optimal big-oh bounds on the expected maximum queue size and on the expected maximum amount of storage used by HwLD in excess of the optimal amount. The techniques appear extendible to other models, such as M/M/1.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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  • 6
    Digitale Medien
    Digitale Medien
    Springer
    Algorithmica 8 (1992), S. 257-283 
    ISSN: 1432-0541
    Schlagwort(e): Computational geometry ; Hidden-line elimination ; Perspective view ; Isothetic rectangles ; Parallelepipeds ; Fractional cascading ; Segment tree ; Range tree ; Dominance relation
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Mathematik
    Notizen: Abstract We present a new hidden-line elemination technique for displaying the perspective view of a scene of three-dimensional isothetic parallelepipeds (3D-rectangles). We assume that the 3D-rectangles are totally ordered based upon the dominance relation of occlusion. The perspective view is generated incrementally, starting with the closest 3D-rectangle and proceeding away from the view point. Our algorithm is scene-sensitive and uses0((n +d) logn log logn) time, wheren is the number of 3D-rectangles andd is the number of edges of the display. This improves over the heretofore best known technique. The primary data structure is an efficient alternative to dynamic fractional cascading for use with augmented segment and range trees when the universe is fixed beforehand. It supports queries inO((logn +k) log logn) time, wherek is the size of the response, and insertions and deletions inO(logn log logn) time, all in the worst case.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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  • 7
    Digitale Medien
    Digitale Medien
    Springer
    Machine learning 29 (1997), S. 65-88 
    ISSN: 0885-6125
    Schlagwort(e): Inference ; Learning ; Maps ; Graphs ; Uncertainty ; Noise
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract In many applications in mobile robotics, it is important for a robot to explore its environment in order to construct a representation of space useful for guiding movement. We refer to such a representation as a map, and the process of constructing a map from a set of measurements as map learning. In this paper, we develop a framework for describing map-learning problems in which the measurements taken by the robot are subject to known errors. We investigate approaches to learning maps under such conditions based on Valiant's probably approximately correct learning model. We focus on the problem of coping with accumulated error in combining local measurements to make global inferences. In one approach, the effects of accumulated error are eliminated by the use of local sensing methods that never mislead but occasionally fail to produce an answer. In another approach, the effects of accumulated error are reduced to acceptable levels by repeated exploration of the area to be learned. We also suggest some insights into why certain existing techniques for map learning perform as well as they do. The learning problems explored in this paper are quite different from most of the classification and boolean-function learning problems appearing in the literature. The methods described, while specific to map learning, suggest directions to take in tackling other learning problems.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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  • 8
    Digitale Medien
    Digitale Medien
    Springer
    Machine learning 17 (1994), S. 143-167 
    ISSN: 0885-6125
    Schlagwort(e): Memory-based learning ; PAC learning ; clustering ; approximation ; linear programming ; relaxation ; covering ; hashing
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract A memory-based learning system is an extended memory management system that decomposes the input space either statically or dynamically into subregions for the purpose of storing and retrieving functional information. The main generalization techniques employed by memory-based learning systems are the nearest-neighbor search, space decomposition techniques, and clustering. Research on memory-based learning is still in its early stage. In particular, there are very few rigorous theoretical results regarding memory requirement, sample size, expected performance, and computational complexity. In this paper, we propose a model for memory-based learning and use it to analyze several methods— ε-covering, hashing, clustering, tree-structured clustering, and receptive-fields—for learning smooth functions. The sample size and system complexity are derived for each method. Our model is built upon the generalized PAC learning model of Haussler (Haussler, 1989) and is closely related to the method of vector quantization in data compression. Our main result is that we can build memory-based learning systems using new clustering algorithms (Lin & Vitter, 1992a) to PAC-learn in polynomial time using only polynomial storage in typical situations.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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  • 9
    Digitale Medien
    Digitale Medien
    Springer
    Machine learning 6 (1991), S. 211-230 
    ISSN: 0885-6125
    Schlagwort(e): Neural nets ; perceptrons ; cascade neural nets ; scaling ; modular neural nets ; learning from examples ; concept learning ; theoretical limitation
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract We consider the computational complexity of learning by neural nets. We are interested in how hard it is to design appropriate neural net architectures and to train neural nets for general and specialized learning tasks. Our main result shows that the training problem for 2-cascade neural nets (which have only two non-input nodes, one of which is hidden) is N℘-complete, which implies that finding an optimal net (in terms of the number of non-input units) that is consistent with a set of examples is also N℘-complete. This result also demonstrates a surprising gap between the computational complexities of one-node (perceptron) and two-node neural net training problems, since the perceptron training problem can be solved in polynomial time by linear programming techniques. We conjecture that training a k-cascade neural net, which is a classical threshold network training problem, is also N℘-complete, for each fixed k≥2. We also show that the problem of finding an optimal perceptron (in terms of the number of non-zero weights) consistent with a set of training examples is N℘-hard. Our neural net learning model encapsulates the idea of modular neural nets, which is a popular approach to overcoming the scaling problem in training neural nets. We investigate how much easier the training problem becomes if the class of concepts to be learned is known a priori and the net architecture is allowed to be sufficiently non-optimal. Finally, we classify several neural net optimization problems within the polynomial-time hierarchy.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 10
    Digitale Medien
    Digitale Medien
    Springer
    Machine learning 6 (1991), S. 211-230 
    ISSN: 0885-6125
    Schlagwort(e): Neural nets ; perceptrons ; cascade neural nets ; scaling ; modular neural nets ; learning from examples ; concept learning ; theoretical limitation
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract We consider the computational complexity of learning by neural nets. We are interested in how hard it is to design appropriate neural net architectures and to train neural nets for general and specialized learning tasks. Our main result shows that the training problem for 2-cascade neural nets (which have only two non-input nodes, one of which is hidden) is $$\Re P$$ -complete, which implies that finding an optimal net (in terms of the number of non-input units) that is consistent with a set of examples is also $$\Re P$$ -complete. This result also demonstrates a surprising gap between the computational complexities of one-node (perceptron) and two-node neural net training problems, since the perceptron training problem can be solved in polynomial time by linear programming techniques. We conjecture that training a k-cascade neural net, which is a classical threshold network training problem, is also $$\Re P$$ -complete, for each fixed k ≥ 2. We also show that the problem of finding an optimal perceptron (in terms of the number of non-zero weights) consistent with a set of training examples is $$\Re P$$ -hard. Our neural net learning model encapsulates the idea of modular neural nets, which is a popular approach to overcoming the scaling problem in training neural nets. We investigate how much easier the training problem becomes if the class of concepts to be learned is known a priori and the net architecture is allowed to be sufficiently non-optimal. Finally, we classify several neural net optimization problems within the polynomial-time hierarchy.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
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