標題: Titlebook: Algorithmic Learning Theory; 17th International C José L. Balcázar,Philip M. Long,Frank Stephan Conference proceedings 2006 Springer-Verlag [打印本頁] 作者: Bush 時間: 2025-3-21 16:50
書目名稱Algorithmic Learning Theory影響因子(影響力)
書目名稱Algorithmic Learning Theory影響因子(影響力)學科排名
書目名稱Algorithmic Learning Theory網絡公開度
書目名稱Algorithmic Learning Theory網絡公開度學科排名
書目名稱Algorithmic Learning Theory被引頻次
書目名稱Algorithmic Learning Theory被引頻次學科排名
書目名稱Algorithmic Learning Theory年度引用
書目名稱Algorithmic Learning Theory年度引用學科排名
書目名稱Algorithmic Learning Theory讀者反饋
書目名稱Algorithmic Learning Theory讀者反饋學科排名
作者: 安裝 時間: 2025-3-21 21:40 作者: 苦澀 時間: 2025-3-22 02:40
e-Science and the Semantic Web: A Symbiotic Relationshipfrastructure that enables this [4]. Scientific progress increasingly depends on pooling know-how and results; making connections between ideas, people, and data; and finding and reusing knowledge and resources generated by others in perhaps unintended ways. It is about harvesting and harnessing the 作者: Definitive 時間: 2025-3-22 05:49 作者: floaters 時間: 2025-3-22 10:43
Data-Driven Discovery Using Probabilistic Hidden Variable Modelsative approach include (a) representing complex stochastic phenomena using the structured language of graphical models, (b) using latent (hidden) variables to make inferences about unobserved phenomena, and (c) leveraging Bayesian ideas for learning and prediction. This talk will begin with a brief 作者: Hla461 時間: 2025-3-22 13:06
Reinforcement Learning and Apprenticeship Learning for Robotic Controlrn reinforcement learning algorithms. Some of the reasons for these problems being challenging are (i) It can be hard to write down, in closed form, a formal specification of the control task (for example, what is the cost function for “driving well”?), (ii) It is often difficult to learn a good mod作者: MAG 時間: 2025-3-22 17:34 作者: 搜集 時間: 2025-3-22 21:23
On Exact Learning Halfspaces with Random Consistent Hypothesis Oracleunterexamples received from the equivalence query oracle. We use the RCH oracle to give a new polynomial time algorithm for exact learning halfspaces from majority of halfspaces and show that its query complexity is less (by some constant factor) than the best known algorithm that learns halfspaces 作者: maudtin 時間: 2025-3-23 04:39
Active Learning in the Non-realizable Caseet function that perfectly classifies all training and test examples. This assumption can hardly ever be justified in practice. In this paper, we study how relaxing the realizability assumption affects the sample complexity of active learning. First, we extend existing results on query learning to s作者: Factorable 時間: 2025-3-23 08:33 作者: 信任 時間: 2025-3-23 12:52 作者: tympanometry 時間: 2025-3-23 16:24
The Complexity of Learning SUBSEQ (,)following inductive inference problem: given .(.), .(0), .(1), .(00), ... learn, in the limit, a DFA for SUBSEQ(.). We consider this model of learning and the variants of it that are usually studied in inductive inference: anomalies, mindchanges, and teams.作者: Hemiparesis 時間: 2025-3-23 20:43
Mind Change Complexity of Inferring Unbounded Unions of Pattern Languages from Positive Datative data with mind change bound between .. and .. We give a very tight bound on the mind change complexity based on the length of the constant segments and the size of the alphabet of the pattern languages. This is, to the authors’ knowledge, the first time a natural class of languages has been sho作者: Myelin 時間: 2025-3-24 01:44 作者: temperate 時間: 2025-3-24 02:23
Iterative Learning from Positive Data and Negative Counterexamplesture with a teacher (oracle) if it is a subset of the target language (and if it is not, then it receives a negative counterexample), and uses only limited long-term memory (incorporated in conjectures). Three variants of this model are compared: when a learner receives least negative counterexample作者: 假裝是你 時間: 2025-3-24 10:04 作者: 蜈蚣 時間: 2025-3-24 11:56
Risk-Sensitive Online Learninghe best trade-off between rewards and .. Motivated by finance applications, we consider two common measures balancing returns and risk: the . [9] and the . criterion of Markowitz [8]. We first provide negative results establishing the impossibility of no-regret algorithms under these measures, thus 作者: irritation 時間: 2025-3-24 17:55
Leading Strategies in Competitive On-Line Predictiony prediction strategies admits a “l(fā)eading prediction strategy”, which not only asymptotically performs at least as well as any continuous limited-memory strategy but also satisfies the property that the excess loss of any continuous limited-memory strategy is determined by how closely it imitates th作者: GREEN 時間: 2025-3-24 22:32
Solving Semi-infinite Linear Programs Using Boosting-Like Methodsg. .=?. In the finite case the constraints can be described by a matrix with . rows and . columns that can be used to directly solve the LP. In semi-infinite linear programs (SILPs) the constraints are often given in a functional form depending on . or implicitly defined, for instance by the outcome of another algorithm.作者: nettle 時間: 2025-3-25 00:51 作者: chlorosis 時間: 2025-3-25 04:00 作者: tic-douloureux 時間: 2025-3-25 10:21
https://doi.org/10.1007/978-3-0348-6479-4t the capabilities of incremental learners. These results may serve as a first step towards characterising the structure of typical classes learnable incrementally and thus towards elaborating uniform incremental learning methods.作者: Osteons 時間: 2025-3-25 13:36
Der Bildhauer Walter Ostermayerly describe some non-trivial connections to (seemingly) different topics in learning theory, complexity theory, and cryptography. A connection to the so-called Hidden Number Problem, which plays an important role for proving bit-security of cryptographic functions, will be discussed in somewhat more detail.作者: Offset 時間: 2025-3-25 17:42
https://doi.org/10.1007/978-3-0348-6479-4t learnable in another one. Some characterizations for learnability of algorithmically enumerable families of languages for the models in question are obtained. Since learnability of any part of the target language does not imply . of the learning process, we consider also our models under additional monotonicity constraint.作者: Dorsal 時間: 2025-3-25 20:10
Spectral Norm in Learning Theory: Some Selected Topicsly describe some non-trivial connections to (seemingly) different topics in learning theory, complexity theory, and cryptography. A connection to the so-called Hidden Number Problem, which plays an important role for proving bit-security of cryptographic functions, will be discussed in somewhat more detail.作者: forestry 時間: 2025-3-26 02:51
Learning and Extending Sublanguagest learnable in another one. Some characterizations for learnability of algorithmically enumerable families of languages for the models in question are obtained. Since learnability of any part of the target language does not imply . of the learning process, we consider also our models under additional monotonicity constraint.作者: Fortify 時間: 2025-3-26 04:52 作者: FIN 時間: 2025-3-26 11:22 作者: ligature 時間: 2025-3-26 14:11 作者: 彎曲道理 時間: 2025-3-26 17:57 作者: 顛簸下上 時間: 2025-3-27 00:46 作者: 圣人 時間: 2025-3-27 04:14
Der Bindegewebsapparat in der Orbita,ontroller for a high-dimensional, stochastic, control task. However, when we are allowed to learn from a human demonstration of a task—in other words, if we are in the apprenticeship learning setting—then a number of efficient algorithms can be used to address each of these problems.作者: 不如樂死去 時間: 2025-3-27 07:12
https://doi.org/10.1007/978-3-662-30030-5r ingredients used to obtain the results stated above are techniques from exact learning [4] and ideas from recent work on learning augmented .. circuits [14] and on representing Boolean functions as thresholds of parities [16].作者: 消息靈通 時間: 2025-3-27 12:26
Vom Kleinbetrieb zur Bleistiftindustrie,er type of well-partial-orderings to obtain a mind change bound. The inference algorithm presented can be easily applied to a wide range of classes of languages. Finally, we show an interesting connection between proof theory and mind change complexity.作者: WITH 時間: 2025-3-27 15:13 作者: FACT 時間: 2025-3-27 19:52
https://doi.org/10.1007/978-3-662-02227-6trategy, in the sense that the loss of any prediction strategy whose norm is not too large is determined by how closely it imitates the leading strategy. This result is extended to the loss functions given by Bregman divergences and by strictly proper scoring rules.作者: 加劇 時間: 2025-3-27 22:39
e-Science and the Semantic Web: A Symbiotic Relationshipmeaning to facilitate sharing and reuse, better enabling computers and people to work in cooperation [1]. Applying the Semantic Web paradigm to e-Science [3] has the potential to bring significant benefits to scientific discovery [2]. We identify the benefits of lightweight and heavyweight approaches, based on our experiences in the Life Sciences.作者: 高歌 時間: 2025-3-28 03:17
Reinforcement Learning and Apprenticeship Learning for Robotic Controlontroller for a high-dimensional, stochastic, control task. However, when we are allowed to learn from a human demonstration of a task—in other words, if we are in the apprenticeship learning setting—then a number of efficient algorithms can be used to address each of these problems.作者: headway 時間: 2025-3-28 09:26
Learning Unions of ,(1)-Dimensional Rectanglesr ingredients used to obtain the results stated above are techniques from exact learning [4] and ideas from recent work on learning augmented .. circuits [14] and on representing Boolean functions as thresholds of parities [16].作者: 自制 時間: 2025-3-28 12:25
Mind Change Complexity of Inferring Unbounded Unions of Pattern Languages from Positive Dataer type of well-partial-orderings to obtain a mind change bound. The inference algorithm presented can be easily applied to a wide range of classes of languages. Finally, we show an interesting connection between proof theory and mind change complexity.作者: 腐敗 時間: 2025-3-28 17:11
Iterative Learning from Positive Data and Negative Counterexamplesvant models of learnability in the limit, study how our model works for indexed classes of recursive languages, and show that learners in our model can work in . way — never abandoning the first right conjecture.作者: 乳白光 時間: 2025-3-28 18:47
Leading Strategies in Competitive On-Line Predictiontrategy, in the sense that the loss of any prediction strategy whose norm is not too large is determined by how closely it imitates the leading strategy. This result is extended to the loss functions given by Bregman divergences and by strictly proper scoring rules.作者: 怪物 時間: 2025-3-29 02:36
Typische Fehler im Vorstellungsgespr?chich then are evaluated with respect to their correctness and wrong predictions (coming from wrong hypotheses) incur some loss on the learner. In the following, a more detailed introduction is given to the five invited talks and then to the regular contributions.作者: 業(yè)余愛好者 時間: 2025-3-29 05:15 作者: 乳汁 時間: 2025-3-29 09:41
https://doi.org/10.1007/978-3-662-02227-6x-year S&P 500 data set and find that the modified best expert algorithm outperforms the traditional with respect to Sharpe ratio, MV, and accumulated wealth. To our knowledge this paper initiates the investigation of explicit risk considerations in the standard models of worst-case online learning.作者: albuminuria 時間: 2025-3-29 13:08 作者: 流動性 時間: 2025-3-29 17:48 作者: minimal 時間: 2025-3-29 21:18
Risk-Sensitive Online Learningx-year S&P 500 data set and find that the modified best expert algorithm outperforms the traditional with respect to Sharpe ratio, MV, and accumulated wealth. To our knowledge this paper initiates the investigation of explicit risk considerations in the standard models of worst-case online learning.作者: Foregery 時間: 2025-3-30 00:12
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/152983.jpg作者: pancreas 時間: 2025-3-30 06:25
https://doi.org/10.1007/11894841Boosting; Support Vector Machine; algorithm; algorithmic learning theory; algorithms; kernel method; learn作者: fastness 時間: 2025-3-30 12:06 作者: ARIA 時間: 2025-3-30 14:21 作者: 附錄 時間: 2025-3-30 18:04
On Exact Learning from Random WalkWe consider a few particular exact learning models based on a random walk stochastic process, and thus more restricted than the well known general exact learning models. We give positive and negative results as to whether learning in these particular models is easier than in the general learning models.作者: 膽汁 時間: 2025-3-30 23:35 作者: irradicable 時間: 2025-3-31 01:57
Teaching Memoryless Randomized Learners Without Feedbackandomized learners is provided and, based on it, optimal teaching teaching times for certain classes are established. Second, the problem of determining the . is shown to be .-hard. Third, an algorithm for approximating the optimal teaching time is given. Finally, two heuristics for teaching are studied, i.e., cyclic teachers and greedy teachers.作者: 向外 時間: 2025-3-31 07:35
