## Learning by Canonical Smooth Estimation, Part II: Learning and Choice of Model Complexity (0)

Venue: | IEEE Transactions on Automatic Control |

Citations: | 13 - 2 self |

### BibTeX

@ARTICLE{Buescher_learningby,

author = {Kevin L. Buescher and P. R. Kumar},

title = {Learning by Canonical Smooth Estimation, Part II: Learning and Choice of Model Complexity},

journal = {IEEE Transactions on Automatic Control},

year = {},

volume = {41},

pages = {557--569}

}

### OpenURL

### Abstract

In this paper, we analyze the properties of a procedure for learning from examples. This "canonical learner" is based on a canonical error estimator developed in a companion paper. In learning problems, we observe data that consists of labeled sample points, and the goal is to find a model, or "hypothesis," from a set of candidates that will accurately predict the labels of new sample points. The expected mismatch between a hypothesis' prediction and the actual label of a new sample point is called the hypothesis ' "generalization error." We compare the canonical learner with the traditional technique of finding hypotheses that minimize the relative frequency-based empirical error estimate. We show that, for a broad class of learning problems, the set of cases for which such empirical error minimization works is a proper subset of the cases for which the canonical learner works. We derive bounds to show that the number of samples required by these two methods is comparable. We also add...

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Citation Context ...k to select the final hypothesis. The underlying intuition is that we should trade some accuracy on the data in exchange for a "simpler" hypothesis. For examples of this method, see [32], [3=-=3], [34], [35]-=-, [4], [13], [36], [37], [38], and [39]. Again, this penalty is determined from some measure of complexity that is derived from the structure of H and must be carefully selected to ensure that learnin... |

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Citation Context ...n . 3.3 The 0/1-valued, Distribution-Free Case We now specialize to the case where the hypotheses and labels are 0/1-valued and P = P , the set of all probability distributions on X \Theta f0; 1g. In =-=[2], Vap-=-nik and Chervonenkis introduced a property of a set of 0/1-valued functions H that determines when (P ; H) is simultaneously estimable by femp . This property has come to be known as the "VapnikC... |

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Citation Context ...ply choosing the hypothesis with the least estimated error, since this hypothesis will also have nearly the least true error. Inspired by the pioneering work of Vapnik and Chervonenkis ([2], [3], and =-=[4]-=-), much research has been done to determine when the empirical error estimate based on the labeled sample ~s(m) = (~x(m); ~y(m)), femp [~s(m); h] = 1 m m X i=1 L(h(x i ); y i ); 2 succeeds in simultan... |

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Citation Context ...erences between g cl and some other procedures for learning in the literature. Also, we discuss how g cl avoids overfitting the data. Superficially, g cl resembles the method of cross-validation (see =-=[6]-=-). However, the hypotheses selected by cross-validation are usually chosen by minimizing the empirical error on a subsample. The use of an empirical cover is clearly a different approach, since the la... |

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Citation Context ...e same order as m femp . 12 Remark 3.1. We can capture the manner in which K(ff) scales with ff by the quantity lim ff&0 log K(ff) log( 1 ff ) : This is the metric dimension of the set Z (see [5] and =-=[14]-=-). It is a way of defining the dimension of a metric space (even when it is not a vector space) by appealing to the notion of volume. For example, if Z is IR n and d is the Euclidean distance, K(ff) i... |

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Citation Context ...e largest n for which there is some ~v(n) 2 V n shattered by B. (If, for arbitrarily large n, there are ~v(n) that are shattered by B, we say that VCdim(B) = 1.) See [2], [16], [17], [4], [18], [15], =-=[19]-=-, [20], [21], and [22] for examples of classes of finite VC-dimension. Using results from [4] and [15], we can bound m femp as follows. Lemma 3.1. With q = VCdim(H); 1sq ! 1, m femp (ffl; ffi; P ; H) ... |

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Citation Context ...] for an overview of these methods). Most of these schemes involve withholding part of the samples and/or resampling the data in some fashion, as in cross-validation ([6] and [36]) and bootstrapping (=-=[41]-=-). A point that we should note here is that femp is still used in many of these methods to select the initial candidate hypotheses h k . The learning procedure we present next, or at least the ideas i... |

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Citation Context ...rge M allows the elements of the cover to be less simple. In some cases, finding the simplest hypothesis consistent with a labeling is much harder than finding one that is only reasonably simple (see =-=[42]-=-). This would dictate using M ? 1. A key observation is that we can construct finite M-simple empirical coverings. Lemma 4.1. Under Assumption 2.2, for any Ms1 we can construct an M-simple empirical f... |

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Citation Context ...nderlying intuition is that we should trade some accuracy on the data in exchange for a "simpler" hypothesis. For examples of this method, see [32], [33], [34], [35], [4], [13], [36], [37], =-=[38], and [39]-=-. Again, this penalty is determined from some measure of complexity that is derived from the structure of H and must be carefully selected to ensure that learning occurs. The two preceding methods hav... |

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Citation Context .... A variety of methods use this same principle, and all of them rely on some connection between the convergence properties of femp and some prior measure of the complexity of hypotheses from H k (see =-=[26]-=-, [27], [28], [29], [30], and [31]). For instance, consider the case in which each H k has finite VC-dimension. Inspection of Lemma 3.1 shows that, if we let k(n) increase slowly enough, then femp sim... |

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Citation Context ...mension of B, VCdim(B), is the largest n for which there is some ~v(n) 2 V n shattered by B. (If, for arbitrarily large n, there are ~v(n) that are shattered by B, we say that VCdim(B) = 1.) See [2], =-=[16]-=-, [17], [4], [18], [15], [19], [20], [21], and [22] for examples of classes of finite VC-dimension. Using results from [4] and [15], we can bound m femp as follows. Lemma 3.1. With q = VCdim(H); 1sq !... |

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Citation Context ...se this same principle, and all of them rely on some connection between the convergence properties of femp and some prior measure of the complexity of hypotheses from H k (see [26], [27], [28], [29], =-=[30]-=-, and [31]). For instance, consider the case in which each H k has finite VC-dimension. Inspection of Lemma 3.1 shows that, if we let k(n) increase slowly enough, then femp simultaneously estimates (P... |

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Citation Context ...mine the case where the hypotheses and labels are real-valued and P = P , the set of all probability distributions on X \Theta Y . Building on the work of Dudley ([16] and [18]) and Pollard ([23] and =-=[24]), Haussle-=-r has made much progress in finding conditions that are sufficient for femp to be a simultaneous estimator (see [25] and [14]). One of these conditions is that a certain "pseudodimension" be... |

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Citation Context ...sis. The underlying intuition is that we should trade some accuracy on the data in exchange for a "simpler" hypothesis. For examples of this method, see [32], [33], [34], [35], [4], [13], [3=-=6], [37], [38]-=-, and [39]. Again, this penalty is determined from some measure of complexity that is derived from the structure of H and must be carefully selected to ensure that learning occurs. The two preceding m... |

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Citation Context ...for which there is some ~v(n) 2 V n shattered by B. (If, for arbitrarily large n, there are ~v(n) that are shattered by B, we say that VCdim(B) = 1.) See [2], [16], [17], [4], [18], [15], [19], [20], =-=[21]-=-, and [22] for examples of classes of finite VC-dimension. Using results from [4] and [15], we can bound m femp as follows. Lemma 3.1. With q = VCdim(H); 1sq ! 1, m femp (ffl; ffi; P ; H) ! q ffl 2 [2... |

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Citation Context ...im(B), is the largest n for which there is some ~v(n) 2 V n shattered by B. (If, for arbitrarily large n, there are ~v(n) that are shattered by B, we say that VCdim(B) = 1.) See [2], [16], [17], [4], =-=[18]-=-, [15], [19], [20], [21], and [22] for examples of classes of finite VC-dimension. Using results from [4] and [15], we can bound m femp as follows. Lemma 3.1. With q = VCdim(H); 1sq ! 1, m femp (ffl; ... |

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Citation Context ...ypothesis. The underlying intuition is that we should trade some accuracy on the data in exchange for a "simpler" hypothesis. For examples of this method, see [32], [33], [34], [35], [4], [1=-=3], [36], [37]-=-, [38], and [39]. Again, this penalty is determined from some measure of complexity that is derived from the structure of H and must be carefully selected to ensure that learning occurs. The two prece... |

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Citation Context ...rn by simply choosing the hypothesis with the least estimated error, since this hypothesis will also have nearly the least true error. Inspired by the pioneering work of Vapnik and Chervonenkis ([2], =-=[3]-=-, and [4]), much research has been done to determine when the empirical error estimate based on the labeled sample ~s(m) = (~x(m); ~y(m)), femp [~s(m); h] = 1 m m X i=1 L(h(x i ); y i ); 2 succeeds in... |

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Citation Context ...there is some ~v(n) 2 V n shattered by B. (If, for arbitrarily large n, there are ~v(n) that are shattered by B, we say that VCdim(B) = 1.) See [2], [16], [17], [4], [18], [15], [19], [20], [21], and =-=[22]-=- for examples of classes of finite VC-dimension. Using results from [4] and [15], we can bound m femp as follows. Lemma 3.1. With q = VCdim(H); 1sq ! 1, m femp (ffl; ffi; P ; H) ! q ffl 2 [20 ln(8=ffl... |

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Citation Context ...est n for which there is some ~v(n) 2 V n shattered by B. (If, for arbitrarily large n, there are ~v(n) that are shattered by B, we say that VCdim(B) = 1.) See [2], [16], [17], [4], [18], [15], [19], =-=[20]-=-, [21], and [22] for examples of classes of finite VC-dimension. Using results from [4] and [15], we can bound m femp as follows. Lemma 3.1. With q = VCdim(H); 1sq ! 1, m femp (ffl; ffi; P ; H) ! q ff... |

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Citation Context ...bsample. The use of an empirical cover is clearly a different approach, since the labels of the first n points are not even used. The learning procedure g cl resembles the cover-based methods in [4], =-=[7]-=-, [8], [9], and [10]. In these methods, knowledge of P or the structure of P is used to select a finite cover for H and empirical estimates are used to select the best element of the cover. Thus, ther... |

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Citation Context ...mportant difference: g cl selects an empirical cover based on the data, whereas the covers in these other methods are fixed in advance. Devroye examines a general structure for learning procedures in =-=[11]. There, a class of -=-candidate hypotheses is selected based on a "training set" (~s 0 in our notation), and the hypothesis with the least empirical error on an independent "testing set" (~s 00 ) is sel... |

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Citation Context ...e Equation (24) as sup h2H \Delta s.t. h(~x(n))= ~ 0 E P h ? ffl: (25) Let q \Delta = VCdim(H \Delta ). By Lemma A.1, q \Delta is finite if q is. Also, it is true that q \Deltas1 if qs1. We have from =-=[47]-=- that (25) holds with probability less than ffi for any P 2 P when n is at least 3 ffl i q \Delta ln(12=ffl) + ln(2=ffi) j : (26) Thus, (26) serves as a bound on N ec (1; ffl; ffi). Replacing ffl and ... |

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Citation Context ...articular interest is to relax the requirement that the labeled samples are drawn independently and are identically distributed. A few papers in the literature do address more general situations; see =-=[44]-=-, [30], [45], and [46]. Also, the learning framework could be extended to a nonparametric setting by allowing the hypothesis class to vary with the observed data (as in nearest neighbor classification... |

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Citation Context ...hods use this same principle, and all of them rely on some connection between the convergence properties of femp and some prior measure of the complexity of hypotheses from H k (see [26], [27], [28], =-=[29]-=-, [30], and [31]). For instance, consider the case in which each H k has finite VC-dimension. Inspection of Lemma 3.1 shows that, if we let k(n) increase slowly enough, then femp simultaneously estima... |

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Citation Context ...n of B, VCdim(B), is the largest n for which there is some ~v(n) 2 V n shattered by B. (If, for arbitrarily large n, there are ~v(n) that are shattered by B, we say that VCdim(B) = 1.) See [2], [16], =-=[17]-=-, [4], [18], [15], [19], [20], [21], and [22] for examples of classes of finite VC-dimension. Using results from [4] and [15], we can bound m femp as follows. Lemma 3.1. With q = VCdim(H); 1sq ! 1, m ... |

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Citation Context ... Y . Building on the work of Dudley ([16] and [18]) and Pollard ([23] and [24]), Haussler has made much progress in finding conditions that are sufficient for femp to be a simultaneous estimator (see =-=[25] and [14])-=-. One of these conditions is that a certain "pseudodimension" be finite. This pseudodimension generalizes the VC-dimension to classes of real-valued functions. (Vapnik generalizes the VC-dim... |

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Citation Context ...iuc.edu 1 1 Introduction This paper develops and investigates the properties of a new class of learning procedures. These procedures are based on the canonical error estimation procedure developed in =-=[1]. We also -=-provide bounds on the number of samples required by a procedure. Additionally, we propose and analyze a method of selecting a hypothesis, or "model," with an appropriate degree of complexity... |

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Citation Context ...f the relative sizes of ~s 0 and ~s 00 as well as a characterization of a condition that is sufficient for this procedure to work. In the 0/1-valued, noise-free case, a scheme akin to g cl is used in =-=[12]-=- to transform a learning procedure for one triple (P; C 1 ; C 1 ) into a learning procedure for another triple 6 (P; C 2 ; C 2 ). If g : ~s 7! H picks a hypothesis that agrees with the data and yet g ... |

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Citation Context ...he use of an empirical cover is clearly a different approach, since the labels of the first n points are not even used. The learning procedure g cl resembles the cover-based methods in [4], [7], [8], =-=[9]-=-, and [10]. In these methods, knowledge of P or the structure of P is used to select a finite cover for H and empirical estimates are used to select the best element of the cover. Thus, there is an im... |

10 |
Ordered risk minimization
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Citation Context ...imized over k to select the final hypothesis. The underlying intuition is that we should trade some accuracy on the data in exchange for a "simpler" hypothesis. For examples of this method, =-=see [32], [33]-=-, [34], [35], [4], [13], [36], [37], [38], and [39]. Again, this penalty is determined from some measure of complexity that is derived from the structure of H and must be carefully selected to ensure ... |

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Citation Context ...le. The use of an empirical cover is clearly a different approach, since the labels of the first n points are not even used. The learning procedure g cl resembles the cover-based methods in [4], [7], =-=[8]-=-, [9], and [10]. In these methods, knowledge of P or the structure of P is used to select a finite cover for H and empirical estimates are used to select the best element of the cover. Thus, there is ... |

9 |
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Citation Context ...inal hypothesis. The underlying intuition is that we should trade some accuracy on the data in exchange for a "simpler" hypothesis. For examples of this method, see [32], [33], [34], [35], [=-=4], [13], [36]-=-, [37], [38], and [39]. Again, this penalty is determined from some measure of complexity that is derived from the structure of H and must be carefully selected to ensure that learning occurs. The two... |

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Citation Context ...enote the smallest ffl-cover of H(~x) by N (ffl; H(~x); ae 1 ). When M(ffl; H(~x); ae 1 ) is finite for every ffl ? 0, as will always be the case when Z = [0; B], the following inequalities hold (see =-=[5]-=-). Lemma A.2. M(2ffl; H(~x); ae 1 )sN (ffl; H(~x); ae 1 )sM(ffl; H(~x); ae 1 ): Finally, we have this result (Theorem 6 of [14]). Lemma A.3. If H(x) ` [0; B] and psdim(H) = q for some 1sq ! 1, then fo... |

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Citation Context ... an empirical cover is clearly a different approach, since the labels of the first n points are not even used. The learning procedure g cl resembles the cover-based methods in [4], [7], [8], [9], and =-=[10]-=-. In these methods, knowledge of P or the structure of P is used to select a finite cover for H and empirical estimates are used to select the best element of the cover. Thus, there is an important di... |

4 |
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Citation Context ...riety of methods use this same principle, and all of them rely on some connection between the convergence properties of femp and some prior measure of the complexity of hypotheses from H k (see [26], =-=[27]-=-, [28], [29], [30], and [31]). For instance, consider the case in which each H k has finite VC-dimension. Inspection of Lemma 3.1 shows that, if we let k(n) increase slowly enough, then femp simultane... |

4 |
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Citation Context ...to relax the requirement that the labeled samples are drawn independently and are identically distributed. A few papers in the literature do address more general situations; see [44], [30], [45], and =-=[46]-=-. Also, the learning framework could be extended to a nonparametric setting by allowing the hypothesis class to vary with the observed data (as in nearest neighbor classification). It might prove usef... |

3 |
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Citation Context ... over k to select the final hypothesis. The underlying intuition is that we should trade some accuracy on the data in exchange for a "simpler" hypothesis. For examples of this method, see [3=-=2], [33], [34]-=-, [35], [4], [13], [36], [37], [38], and [39]. Again, this penalty is determined from some measure of complexity that is derived from the structure of H and must be carefully selected to ensure that l... |

2 |
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Citation Context ... For instance, consider the case where the hypotheses and labels are real-valued and the distance measure d(z 1 ; z 2 ) = jz 1 \Gamma z 2 j. Using results from [4], it is straightforward to show (see =-=[43]-=-) that E ~x converges simultaneously over (P; d(H i ; H i )) 20 whenever it does so over (P; H i ). Thus, when VCdim(H i ) or psdim(H i ) is finite for each i (as is the case for many parametric model... |

2 |
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Citation Context ...terest is to relax the requirement that the labeled samples are drawn independently and are identically distributed. A few papers in the literature do address more general situations; see [44], [30], =-=[45]-=-, and [46]. Also, the learning framework could be extended to a nonparametric setting by allowing the hypothesis class to vary with the observed data (as in nearest neighbor classification). It might ... |

1 |
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Citation Context ...of methods use this same principle, and all of them rely on some connection between the convergence properties of femp and some prior measure of the complexity of hypotheses from H k (see [26], [27], =-=[28]-=-, [29], [30], and [31]). For instance, consider the case in which each H k has finite VC-dimension. Inspection of Lemma 3.1 shows that, if we let k(n) increase slowly enough, then femp simultaneously ... |

1 |
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Citation Context ...me principle, and all of them rely on some connection between the convergence properties of femp and some prior measure of the complexity of hypotheses from H k (see [26], [27], [28], [29], [30], and =-=[31]-=-). For instance, consider the case in which each H k has finite VC-dimension. Inspection of Lemma 3.1 shows that, if we let k(n) increase slowly enough, then femp simultaneously estimates (P ; H k(n) ... |