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Small Subsets of the Reals . . .
, 2003
"... We discuss the question of which properties of smallness in the sense of measure and category (e.g. being a universally null, perfectly meager or strongly null set) imply the properties of smallness related to some tree forcing notions (e.g. the properties of being Lavernull or Millernull). ..."
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We discuss the question of which properties of smallness in the sense of measure and category (e.g. being a universally null, perfectly meager or strongly null set) imply the properties of smallness related to some tree forcing notions (e.g. the properties of being Lavernull or Millernull).
Mixing operators and small subsets of the circle
, 2011
"... Abstract. We provide complete characterizations, on Banach spaces with cotype 2, of those linear operators which happen to be weakly mixing or strongly mixing transformations with respect to some nondegenerate Gaussian measure. These characterizations involve two families of small subsets of the ci ..."
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Cited by 4 (1 self)
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Abstract. We provide complete characterizations, on Banach spaces with cotype 2, of those linear operators which happen to be weakly mixing or strongly mixing transformations with respect to some nondegenerate Gaussian measure. These characterizations involve two families of small subsets
Wrappers for Feature Subset Selection
 AIJ SPECIAL ISSUE ON RELEVANCE
, 1997
"... In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a ..."
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Cited by 1522 (3 self)
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In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set
Irrelevant Features and the Subset Selection Problem
 MACHINE LEARNING: PROCEEDINGS OF THE ELEVENTH INTERNATIONAL
, 1994
"... We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small highaccuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features ..."
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Cited by 741 (26 self)
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We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small highaccuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features
Propensity Score Matching Methods For NonExperimental Causal Studies
, 2002
"... This paper considers causal inference and sample selection bias in nonexperimental settings in which: (i) few units in the nonexperimental comparison group are comparable to the treatment units; and (ii) selecting a subset of comparison units similar to the treatment units is difficult because uni ..."
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Cited by 690 (3 self)
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from the CPS and PSID, and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. For both comparison groups, we show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated
Initializing RBFNetworks with Small Subsets of Training Examples
"... An important research issue in RBF networks is how to determine the gaussian centers of the radialbasis functions. We investigate a technique that identifies these centers with carefully selected training examples, with the objective to minimize the network's size. The essence is to select thr ..."
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three very small subsets rather than one larger subset whose size would exceed the size of the three small subsets unified. The subsets complement each other in the sense that when used by a nearestneighbor classifier, each of them incurs errors in a different part of the instance space. The paper
Small Byzantine Quorum Systems
 DISTRIBUTED COMPUTING
, 2001
"... In this paper we present two protocols for asynchronous Byzantine Quorum Systems (BQS) built on top of reliable channelsone for selfverifying data and the other for any data. Our protocols tolerate Byzantine failures with fewer servers than existing solutions by eliminating nonessential work in ..."
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Cited by 483 (49 self)
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In this paper we present two protocols for asynchronous Byzantine Quorum Systems (BQS) built on top of reliable channelsone for selfverifying data and the other for any data. Our protocols tolerate Byzantine failures with fewer servers than existing solutions by eliminating nonessential work in the write protocol and by using read and write quorums of different sizes. Since engineering a reliable network layer on an unreliable network is difficult, two other possibilities must be explored. The first is to strengthen the model by allowing synchronous networks that use timeouts to identify failed links or machines. We consider running synchronous and asynchronous Byzantine Quorum protocols over synchronous networks and conclude that, surprisingly, "selftiming" asynchronous Byzantine protocols may offer significant advantages for many synchronous networks when network timeouts are long. We show how to extend an existing Byzantine Quorum protocol to eliminate its dependency on reliable networking and to handle message loss and retransmission explicitly.
Hierarchically Classifying Documents Using Very Few Words
, 1997
"... The proliferation of topic hierarchies for text documents has resulted in a need for tools that automatically classify new documents within such hierarchies. Existing classification schemes which ignore the hierarchical structure and treat the topics as separate classes are often inadequate in text ..."
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Cited by 521 (8 self)
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classifier only examines a small subset. The use of reduced feature sets allows us to util...
Small subsets inherit sparse εregularity
 J. COMBIN. THEORY SER. B
, 2004
"... In this paper we investigate the behaviour of subgraphs of sparse εregular bipartite graphs G = (V1 ∪ V2, E) with vanishing density d that are induced by small subsets of vertices. In particular, we show that, with overwhelming probability, a random set S ⊆ V1 of size s ≫ 1/d contains a subset S ′ ..."
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Cited by 7 (5 self)
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In this paper we investigate the behaviour of subgraphs of sparse εregular bipartite graphs G = (V1 ∪ V2, E) with vanishing density d that are induced by small subsets of vertices. In particular, we show that, with overwhelming probability, a random set S ⊆ V1 of size s ≫ 1/d contains a subset
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 766 (29 self)
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propose a method to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length
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