作者: 蛛絲 時(shí)間: 2025-3-21 23:09 作者: lymphedema 時(shí)間: 2025-3-22 04:28 作者: 不真 時(shí)間: 2025-3-22 07:24
András Gy?rgy,Tamás Linder,Gábor Lugosiion-making.Reviews the similarities and differences of modelThis classroom-texted textbook/reference presents a set of useful modeling techniques, describing how these can be combined into a powerful framework for the analysis and design of business systems. These techniques follow an interactive mo作者: 山間窄路 時(shí)間: 2025-3-22 10:22 作者: 膽小鬼 時(shí)間: 2025-3-22 15:32
Baruch Awerbuch,Robert D. Kleinbergion-making.Reviews the similarities and differences of modelThis classroom-texted textbook/reference presents a set of useful modeling techniques, describing how these can be combined into a powerful framework for the analysis and design of business systems. These techniques follow an interactive mo作者: Hyperalgesia 時(shí)間: 2025-3-22 20:17 作者: conquer 時(shí)間: 2025-3-22 23:19
Shai Shalev-Shwartz,Yoram Singereved without communication of parties. Communication is inherently nondeterministic as it depends on the communication infrastructure. Therefore, modeling of communication is supported with a specific abstraction, which restricts the nondeterminism of communication. This chapter presents two forms o作者: WAIL 時(shí)間: 2025-3-23 05:21
Ranking and Scoring Using Empirical Risk Minimization the ranking risk. The empirical estimates are of the form of a .-statistic. Inequalities from the theory of .-statistics and .-processes are used to obtain performance bounds for the empirical risk minimizers. Convex risk minimization methods are also studied to give a theoretical framework for ran作者: Onerous 時(shí)間: 2025-3-23 06:48 作者: 斑駁 時(shí)間: 2025-3-23 10:36
Stability and Generalization of Bipartite Ranking Algorithmscently gained attention in machine learning. We study generalization properties of ranking algorithms, in a particular setting of the ranking problem known as the bipartite ranking problem, using the notion of algorithmic stability.In particular, we derive generalization bounds for bipartite ranking作者: CRUMB 時(shí)間: 2025-3-23 16:25
Loss Bounds for Online Category Rankinggorithms for . category ranking where the instances are revealed in a sequential manner. We describe additive and multiplicative updates which constitute the core of the learning algorithms. The updates are derived by casting a constrained optimization problem for each new instance. We derive loss b作者: 無(wú)表情 時(shí)間: 2025-3-23 19:33
Margin-Based Ranking Meets Boosting in the Middlee. Our bound suggests that algorithms that maximize the ranking margin generalize well..We then describe a new algorithm, Smooth Margin Ranking, that precisely converges to a maximum ranking-margin solution. The algorithm is a modification of RankBoost, analogous to Approximate Coordinate Ascent Boo作者: IDEAS 時(shí)間: 2025-3-24 00:47
The Value of Agreement, a New Boosting Algorithmfier’s quality and thus reduce the number of labeled examples necessary for achieving it. This is achieved by demanding from the algorithms generating the classifiers to agree on the unlabeled examples. The extent of this improvement depends on the diversity of the learners—a more diverse group of l作者: Instantaneous 時(shí)間: 2025-3-24 03:17 作者: conscience 時(shí)間: 2025-3-24 08:04
Generalization Error Bounds Using Unlabeled Dataement probability of pairs of classifiers using unlabeled data. The first method works in the realizable case. It suggests how the ERM principle can be refined using unlabeled data and has provable optimality guarantees when the number of unlabeled examples is large. Furthermore, the technique exten作者: 薄膜 時(shí)間: 2025-3-24 14:09
On the Consistency of Multiclass Classification Methodsproperty of Bayes consistency. We provide a necessary and sufficient condition for consistency which applies to a large class of multiclass classification methods. The approach is illustrated by applying it to some multiclass methods proposed in the literature.作者: 邊緣 時(shí)間: 2025-3-24 16:03 作者: Analogy 時(shí)間: 2025-3-24 20:10 作者: synovium 時(shí)間: 2025-3-25 00:49
Tracking the Best of Many Experts provided that the set of experts has a certain structure allowing efficient implementations of the exponentially weighted average predictor. As an example we work out the case where each expert is represented by a path in a directed graph and the loss of each expert is the sum of the weights over t作者: Noctambulant 時(shí)間: 2025-3-25 06:35 作者: 處理 時(shí)間: 2025-3-25 08:21
Competitive Collaborative Learning.We formulate such learning tasks as an algorithmic problem based on the multi-armed bandit problem, but with a set of users (as opposed to a single user), of whom a constant fraction are honest and are partitioned into coalitions such that the users in a coalition perceive the same expected quality作者: airborne 時(shí)間: 2025-3-25 13:19
Analysis of Perceptron-Based Active Learning.. We then present a simple selective sampling algorithm for this problem, which combines a modification of the perceptron update with an adaptive filtering rule for deciding which points to query. For data distributed uniformly over the unit sphere, we show that our algorithm reaches generalization作者: Thyroxine 時(shí)間: 2025-3-25 17:59
A New Perspective on an Old Perceptron Algorithmller than a predefined value. We derive worst case mistake bounds for our algorithm. As a byproduct we obtain a new mistake bound for the Perceptron algorithm in the inseparable case. We describe a multiclass extension of the algorithm. This extension is used in an experimental evaluation in which w作者: myalgia 時(shí)間: 2025-3-25 22:36 作者: 憲法沒(méi)有 時(shí)間: 2025-3-26 01:34
Learnability of Bipartite Ranking Functionsss of ranking functions . that we term the rank dimension of ., and show that . is learnable only if its rank dimension is finite. Finally, we investigate questions of the computational complexity of learning ranking functions.作者: 協(xié)議 時(shí)間: 2025-3-26 06:23
A PAC-Style Model for Learning from Labeled and Unlabeled Datas one to estimate compatibility over the space of hypotheses, and reduce the size of the search space to those that, according to one’s assumptions, are a-priori reasonable with respect to the distribution. We discuss a number of technical issues that arise in this context, and provide sample-comple作者: 騷動(dòng) 時(shí)間: 2025-3-26 11:13
Generalization Error Bounds Using Unlabeled Dataor classifiers learned based on all the labeled data. The bound is easy to implement and apply and should be tight whenever cross-validation makes sense. Applying the bound to SVMs on the MNIST benchmark data set gives results that suggest that the bound may be tight enough to be useful in practice.作者: GLADE 時(shí)間: 2025-3-26 13:10
Conference proceedings 2005ear was exceptionally high. In addition to the classical COLT topics, we found an increase in the number of submissions related to novel classi?cation scenarios such as ranking. This - crease re?ects a healthy shift towards more structured classi?cation problems, which are becoming increasingly rele作者: invade 時(shí)間: 2025-3-26 18:18
András Gy?rgy,Tamás Linder,Gábor Lugosi for business system design, with a focus on performance management, motivation modeling, and communication; includes review questions and exercises at the endof each chapter..978-3-319-79213-2978-3-319-15102-1Series ISSN 2195-2817 Series E-ISSN 2195-2825 作者: MEEK 時(shí)間: 2025-3-26 21:00
Nicolò Cesa-Bianchi,Yishay Mansour,Gilles Stoltz for business system design, with a focus on performance management, motivation modeling, and communication; includes review questions and exercises at the endof each chapter..978-3-319-79213-2978-3-319-15102-1Series ISSN 2195-2817 Series E-ISSN 2195-2825 作者: 多產(chǎn)魚(yú) 時(shí)間: 2025-3-27 03:16
Baruch Awerbuch,Robert D. Kleinberg for business system design, with a focus on performance management, motivation modeling, and communication; includes review questions and exercises at the endof each chapter..978-3-319-79213-2978-3-319-15102-1Series ISSN 2195-2817 Series E-ISSN 2195-2825 作者: 浪費(fèi)物質(zhì) 時(shí)間: 2025-3-27 06:19 作者: Nebulizer 時(shí)間: 2025-3-27 13:27
John Langford,Alina Beygelzimerscenarios which could improve students’ achievements. In order to prepare VR scenarios, here in this paper we have started with formulating VR scenario’s criteria which criteria are based on the answers from the interviews recently conducted with STEM teachers.作者: Efflorescent 時(shí)間: 2025-3-27 16:28 作者: 津貼 時(shí)間: 2025-3-27 18:23
Yuri Kalnishkan,Michael V. Vyugin Play was carried out in order to record all the corresponding results which were then categorized and discussed upon. Furthermore, the paper tries to shed light to the perspective of an inexperienced teacher who has the means and the will to integrate mobile devices in his/her classroom, by identif作者: nuclear-tests 時(shí)間: 2025-3-28 01:55
Shai Shalev-Shwartz,Yoram Singerls of different type of choreography, including the choreographies composed from known and unknown number of parallel processes. These examples reveal some open research problems of choreography design and invite the reader to apply Interactive Modeling and Simulation in research of compositional ch作者: corn732 時(shí)間: 2025-3-28 03:43 作者: Functional 時(shí)間: 2025-3-28 09:51
Cynthia Rudin,Corinna Cortes,Mehryar Mohri,Robert E. Schapire作者: 蟄伏 時(shí)間: 2025-3-28 11:42 作者: 充氣女 時(shí)間: 2025-3-28 17:50
978-3-540-26556-6Springer-Verlag Berlin Heidelberg 2005作者: 瑣事 時(shí)間: 2025-3-28 21:05
Learning Theory978-3-540-31892-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 冷漠 時(shí)間: 2025-3-29 01:17
On the Consistency of Multiclass Classification Methodsproperty of Bayes consistency. We provide a necessary and sufficient condition for consistency which applies to a large class of multiclass classification methods. The approach is illustrated by applying it to some multiclass methods proposed in the literature.作者: 怒目而視 時(shí)間: 2025-3-29 05:56
Data Dependent Concentration Bounds for Sequential Prediction Algorithmsg some newly developed probability inequalities, we are able to bound the total generalization performance of a learning algorithm in terms of its observed total loss. Consequences of this analysis will be illustrated with examples.作者: 防銹 時(shí)間: 2025-3-29 07:17
The Weak Aggregating Algorithm and Weak Mixabilityrom a finite alphabet. For the bounded games the paper introduces the Weak Aggregating Algorithm that allows us to obtain additive terms of the form .. A modification of the Weak Aggregating Algorithm that covers unbounded games is also described.作者: fastness 時(shí)間: 2025-3-29 14:06
Tracking the Best of Many Experts provided that the set of experts has a certain structure allowing efficient implementations of the exponentially weighted average predictor. As an example we work out the case where each expert is represented by a path in a directed graph and the loss of each expert is the sum of the weights over the edges in the path.作者: 話 時(shí)間: 2025-3-29 17:57
https://doi.org/10.1007/b137542Boosting; Support Vector Machine; classification; game theory; learning; learning theory; supervised learn作者: Allure 時(shí)間: 2025-3-29 23:30
Martingale BoostingMartingale boosting is a simple and easily understood technique with a simple and easily understood analysis. A slight variant of the approach provably achieves optimal accuracy in the presence of random misclassification noise.作者: custody 時(shí)間: 2025-3-30 01:14
Sensitive Error Correcting Output CodesWe present a reduction from cost-sensitive classification to binary classification based on (a modification of) error correcting output codes. The reduction satisfies the property that . regret for binary classification implies ..-regret of at most 2. for cost estimation. This has several implications:作者: Brain-Imaging 時(shí)間: 2025-3-30 08:00
Margin-Based Ranking Meets Boosting in the MiddleUC and achieves the same AUC as RankBoost. This explains the empirical observations made by Cortes and Mohri, and Caruana and Niculescu-Mizil, about the excellent performance of AdaBoost as a ranking algorithm, as measured by the AUC.作者: electrolyte 時(shí)間: 2025-3-30 10:29
Stability and Generalization of Bipartite Ranking Algorithmsn bounds for ranking, which are based on uniform convergence and in many cases cannot be applied to these algorithms. A comparison of the bounds we obtain with corresponding bounds for classification algorithms yields some interesting insights into the difference in generalization behaviour between ranking and classification.作者: 青春期 時(shí)間: 2025-3-30 13:16 作者: pancreas 時(shí)間: 2025-3-30 17:11
Conference proceedings 2005ning Theory) held in Bertinoro, Italy from June 27 to 30, 2005. The technical program contained 45 papers selected from 120 submissions, 3 open problems selected from among 5 contributed, and 2 invited lectures. The invited lectures were given by Sergiu Hart on “Uncoupled Dynamics and Nash Equilibri作者: TIA742 時(shí)間: 2025-3-30 23:32
A New Perspective on an Old Perceptron Algorithmlgorithm in the inseparable case. We describe a multiclass extension of the algorithm. This extension is used in an experimental evaluation in which we compare the proposed algorithm to the Perceptron algorithm.作者: 內(nèi)疚 時(shí)間: 2025-3-31 03:40 作者: 吹氣 時(shí)間: 2025-3-31 08:06
Ranking and Scoring Using Empirical Risk Minimizationking algorithms based on boosting and support vector machines. Just like in binary classification, fast rates of convergence are achieved under certain noise assumption. General sufficient conditions are proposed in several special cases that guarantee fast rates of convergence.作者: indecipherable 時(shí)間: 2025-3-31 12:35
Loss Bounds for Online Category Rankingounds for the algorithms by using the properties of the dual solution while imposing additional constraints on the dual form. Finally, we outline and analyze the convergence of a general update that can be employed with any Bregman divergence.作者: endoscopy 時(shí)間: 2025-3-31 15:30
The Value of Agreement, a New Boosting Algorithmearners will result in a larger improvement whereas using two copies of a single algorithm gives no advantage at all. As a proof of concept, we apply the algorithm, named AgreementBoost, to a web classification problem where an up to 40% reduction in the number of labeled examples is obtained.
