作者: 轉(zhuǎn)折點(diǎn) 時(shí)間: 2025-3-21 21:29 作者: 破譯密碼 時(shí)間: 2025-3-22 03:45 作者: PANEL 時(shí)間: 2025-3-22 08:36
https://doi.org/10.1007/978-3-322-87794-9hted average of the experts’ predictions. We show that for a large class of loss functions, even with the simplified prediction rule the additional loss of the algorithm over the loss of the best expert is at most . ln ., where . is the number of experts and . a constant that depends on the loss fun作者: cleaver 時(shí)間: 2025-3-22 11:46
https://doi.org/10.1007/978-3-658-00686-0operly between hyperrobust Ex-learning and hyperrobust BC-learning. Furthermore, the bounded totally reliably BC-learnable classes are characterized in terms of infinite branches of certain enumerable families of bounded recursive trees. A class of infinite branches of a further family of trees sepa作者: giggle 時(shí)間: 2025-3-22 15:06
https://doi.org/10.1007/978-3-658-00686-0lds a uniformly decidable family of languages and has effective bounded finite thickness, then for each natural number . > 0, the class of languages defined by formal systems of length ≤ .:.The above sufficient conditions are employed to give an ordinal mind change bound for learnability of minimal 作者: giggle 時(shí)間: 2025-3-22 19:05
Open Theoretical Questions in Reinforcement Learning in a given state and ending upon arrival in a terminal state, terminating the series above. In other cases the interaction is continual, without interruption, and the sum may have an infinite number of terms (in which case we usually assume γ < 1). Infinite horizon cases with γ = 1 are also possibl作者: 新鮮 時(shí)間: 2025-3-22 21:59 作者: 手榴彈 時(shí)間: 2025-3-23 04:02 作者: 寵愛(ài) 時(shí)間: 2025-3-23 09:25
Averaging Expert Predictionshted average of the experts’ predictions. We show that for a large class of loss functions, even with the simplified prediction rule the additional loss of the algorithm over the loss of the best expert is at most . ln ., where . is the number of experts and . a constant that depends on the loss fun作者: chastise 時(shí)間: 2025-3-23 13:08 作者: 辮子帶來(lái)幫助 時(shí)間: 2025-3-23 15:18 作者: finite 時(shí)間: 2025-3-23 20:49 作者: Commonwealth 時(shí)間: 2025-3-23 23:32
Second-Order-Faktorenanalyse (SFA)nits, it is NP-hard to find such a network that makes mistakes on a proportion smaller than .. of the examples, for some constant .. We prove a similar result for the problem of approximately minimizing the quadratic loss of a two-layer network with a sigmoid output unit.作者: 極微小 時(shí)間: 2025-3-24 04:53 作者: 包庇 時(shí)間: 2025-3-24 08:10 作者: 同步左右 時(shí)間: 2025-3-24 11:28 作者: 軍火 時(shí)間: 2025-3-24 16:49 作者: Anticoagulants 時(shí)間: 2025-3-24 19:05 作者: 創(chuàng)作 時(shí)間: 2025-3-25 00:51
Regularized Principal Manifoldsapproach. 2) We derive uniform convergence bounds and hence bounds on the learning rates of the algorithm. In particular, we give good bounds on the covering numbers which allows us to obtain a nearly optimal learning rate of order . for certain types of regularization operators, where . is the sample size and α an arbitrary positive constant.作者: 動(dòng)作謎 時(shí)間: 2025-3-25 05:47 作者: Mumble 時(shí)間: 2025-3-25 09:45 作者: Cholagogue 時(shí)間: 2025-3-25 13:29 作者: Ambulatory 時(shí)間: 2025-3-25 19:02 作者: 量被毀壞 時(shí)間: 2025-3-25 20:15
Strukturiert es Programmieren in Cnsion with respect to the average case. We show that the teaching complexity in the best case is bounded by the self-directed learning complexity. It is also bounded by the VCdimension, if the concept class is intersection-closed. This does not hold for arbitrary concept classes. We find examples which substantiate this gap.作者: 忘恩負(fù)義的人 時(shí)間: 2025-3-26 00:40
Learnability of Quantified Formulasroperty of the basis of relations, their clone of polymorphisms. Finally, we use this technique to give a simpler proof of the already known dichotomy theorem over boolean domains and we present an extension of this theorem to bases with infinite size.作者: 柔美流暢 時(shí)間: 2025-3-26 06:12 作者: 人造 時(shí)間: 2025-3-26 11:09
A Geometric Approach to Leveraging Weak Learners For this potential function, the direction of steepest descent can have negative components. Therefore we provide two transformations for obtaining suitable distributions from these directions of steepest descent. The resulting algorithms have bounds that are incomparable to AdaBoost’s, and their empirical performance is similar to AdaBoost’s.作者: Brochure 時(shí)間: 2025-3-26 13:26
Hardness Results for Neural Network Approximation Problemsnits, it is NP-hard to find such a network that makes mistakes on a proportion smaller than .. of the examples, for some constant .. We prove a similar result for the problem of approximately minimizing the quadratic loss of a two-layer network with a sigmoid output unit.作者: 配置 時(shí)間: 2025-3-26 17:16
Learning Range Restricted Horn Expressionser utilises a previous result on learning function free Horn expressions. This is done by using techniques for flattening and unflattening of examples and clauses, and a procedure for model finding for range restricted expressions. This procedure can also be used to solve the implication problem for this class.作者: Gesture 時(shí)間: 2025-3-26 21:42
https://doi.org/10.1007/3-540-49097-3Algorithmic Learning; Computational Learning; Inductive Inference; Online Learning; learning; learning th作者: BUCK 時(shí)間: 2025-3-27 01:52 作者: AIL 時(shí)間: 2025-3-27 06:14
Theoretical Views of Boostingey theoretical work on boosting including analyses of AdaBoost’s training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass classification problems. We also briefly mention some empirical work.作者: 引起痛苦 時(shí)間: 2025-3-27 09:35
Learning Multiplicity Automata from Smallest Counterexamples improves on an earlier result of Bergadano and Varricchio. A unique representation for MAs is introduced. Our algorithm learns this representation. We also show that any learning algorithm for MAs needs at least . smallest counterexamples. Thus our upper bound on the number of counterexamples cannot be improved substantially.作者: 名次后綴 時(shí)間: 2025-3-27 15:36 作者: 產(chǎn)生 時(shí)間: 2025-3-27 21:33
Lower Bounds on the Rate of Convergence of Nonparametric Pattern Recognitionch are arbitrarily close to Yang’s minimax lower bounds, if the a posteriori probability function is in the classes used by Stone and others. The rates equal to the ones on the corresponding regression estimation problem. Thus for these classes classification is not easier than regression estimation either in individual sense.作者: largesse 時(shí)間: 2025-3-28 00:52 作者: 值得尊敬 時(shí)間: 2025-3-28 02:21
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/232577.jpg作者: 明智的人 時(shí)間: 2025-3-28 06:49
Modifikation der Modellstrukturey theoretical work on boosting including analyses of AdaBoost’s training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass classification problems. We also briefly mention some 作者: 季雨 時(shí)間: 2025-3-28 12:43 作者: 高調(diào) 時(shí)間: 2025-3-28 17:31 作者: 眨眼 時(shí)間: 2025-3-28 22:46 作者: 背心 時(shí)間: 2025-3-29 00:04
Second-Order-Faktorenanalyse (SFA)d size that approximately minimizes the proportion of misclassified examples in a training set, even if there is a network that correctly classifies all of the training examples. In particular, for a training set that is correctly classified by some two-layer linear threshold network with . hidden u作者: 付出 時(shí)間: 2025-3-29 03:14 作者: 媽媽不開(kāi)心 時(shí)間: 2025-3-29 10:26 作者: Commentary 時(shí)間: 2025-3-29 13:59
Thomas Zumbroich,Andreas Mülleres alone and a . sized decision tree representation of the function constructed, in polynomial time. In contrast, such a function cannot be exactly learned with equivalence queries alone using general decision trees and other representation classes as hypotheses..Our results imply others which may b作者: 飛來(lái)飛去真休 時(shí)間: 2025-3-29 18:27 作者: 配置 時(shí)間: 2025-3-29 21:02
Das Verfahren der Gew?sserstrukturkartierungressions, where every term in the consequent of every clause appears also in the antecedent of the clause, is learnable. The result holds both for the model where interpretations are examples (learning from interpretations) and the model where clauses are examples (learning from entailment)..The pap作者: Glutinous 時(shí)間: 2025-3-30 03:43 作者: Axillary 時(shí)間: 2025-3-30 06:49
,Umsetzung des Entwurfs in Prim?rcode,Such algorithms use roughly .(..) weights which can be prohibitively expensive. Surprisingly, algorithms like Winnow require only . weights (one per variable) and the mistake bound of these algorithms is not too much worse than the mistake bound of the more costly algorithms. The purpose of this pap作者: Immobilize 時(shí)間: 2025-3-30 09:50 作者: 辯論的終結(jié) 時(shí)間: 2025-3-30 15:57 作者: 拒絕 時(shí)間: 2025-3-30 18:46
https://doi.org/10.1007/978-3-658-00686-0their images under primitive recursive operators. The following is shown: This notion of learnability does not change if the class of primitive recursive operators is replaced by a larger enumerable class of operators. A class is hyperrobustly Ex-learnable iff it is a subclass of a recursively enume作者: Vldl379 時(shí)間: 2025-3-30 21:05
https://doi.org/10.1007/978-3-658-00686-0ind change complexity bounds for learnability of these classes both from positive facts and from positive and negative facts..Building on Angluin’s notion of finite thickness and Wright’s work on finite elasticity, Shinohara defined the property of bounded finite thickness to give a sufficient condi作者: 慎重 時(shí)間: 2025-3-31 01:40
,, — Anlagen zur Montage fl?chiger Bauteile,strictions. This allows the use of tools such as regularization from the theory of (supervised) risk minimization for unsupervised settings. Moreover, this setting is very closely related to both principal curves and the generative topographic map..We explore this connection in two ways: 1) we propo作者: 苦笑 時(shí)間: 2025-3-31 07:25 作者: 不能約 時(shí)間: 2025-3-31 09:15
https://doi.org/10.1007/978-3-642-96455-8ch are arbitrarily close to Yang’s minimax lower bounds, if the a posteriori probability function is in the classes used by Stone and others. The rates equal to the ones on the corresponding regression estimation problem. Thus for these classes classification is not easier than regression estimation作者: 金絲雀 時(shí)間: 2025-3-31 15:34 作者: gangrene 時(shí)間: 2025-3-31 19:53
0302-9743 Overview: Includes supplementary material: 978-3-540-65701-9978-3-540-49097-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 傾聽(tīng) 時(shí)間: 2025-4-1 00:54
Modifikation der Modellstrukturey theoretical work on boosting including analyses of AdaBoost’s training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass classification problems. We also briefly mention some empirical work.作者: 不發(fā)音 時(shí)間: 2025-4-1 03:44 作者: Spangle 時(shí)間: 2025-4-1 08:12 作者: Cognizance 時(shí)間: 2025-4-1 12:32
https://doi.org/10.1007/978-3-642-96455-8ch are arbitrarily close to Yang’s minimax lower bounds, if the a posteriori probability function is in the classes used by Stone and others. The rates equal to the ones on the corresponding regression estimation problem. Thus for these classes classification is not easier than regression estimation either in individual sense.