作者: Abbreviate 時間: 2025-3-21 20:47 作者: preeclampsia 時間: 2025-3-22 02:43 作者: CUMB 時間: 2025-3-22 05:16 作者: 欺騙世家 時間: 2025-3-22 12:14
https://doi.org/10.1007/978-1-4615-0961-5a very simple, constraint-based model of this hypothesis, more sophisticated versions have been developed within different formal frameworks of approximate reasoning and reasoning under uncertainty. Let us again highlight the following properties of our approaches:作者: 極少 時間: 2025-3-22 15:22
Book 2007tificial intelligence. The key idea of CBR is to tackle new problems by referring to similar problems that have already been solved in the past. More precisely, CBR proceeds from individual experiences in the form of cases. The generalization beyond these experiences typically relies on a kind of re作者: 極少 時間: 2025-3-22 17:10 作者: 的是兄弟 時間: 2025-3-23 00:53
Ontology — Definition & Overviewe of fuzzy set theory in case indexing and retrieval [209, 214], the case-based learning of fuzzy concepts from fuzzy examples [295], the use of fuzzy predicates in the derivation of similarities [40], and the integration of case-based and rule-based reasoning [138]. See [45, 49] for a more general framework of analogical reasoning.作者: 成績上升 時間: 2025-3-23 02:12 作者: 閑蕩 時間: 2025-3-23 09:18 作者: 尊重 時間: 2025-3-23 12:48
Constraint-Based Modeling of Case-Based Inference,arity of associated outcomes in the form of a lower bound. A related inference mechanism then allows for realizing CBI as a kind of constraint propagation. We also discuss representational issues and algorithms for putting the idea of . within this framework into action. The chapter is organized as 作者: 小口啜飲 時間: 2025-3-23 16:06 作者: ABOUT 時間: 2025-3-23 19:24
Conclusions and Outlook,ved cases (a case base consisting of input-output tuples) in order to predict a set of promising candidate outputs given a new query input. The corresponding inference schemes are based on suitable formalizations of the heuristic assumption that similar inputs yield similar outputs. Proceeding from 作者: TERRA 時間: 2025-3-23 22:17
A Framework for Ontology Learningarity of associated outcomes in the form of a lower bound. A related inference mechanism then allows for realizing CBI as a kind of constraint propagation. We also discuss representational issues and algorithms for putting the idea of . within this framework into action. The chapter is organized as 作者: 失望未來 時間: 2025-3-24 04:13
Ontology — Definition & Overviewing has been pointed out recently [99, 407]. Besides, some attempts at combining case-based reasoning (or, more generally, analogical reasoning) and methods from fuzzy set theory have already been made [408], including the use of fuzzy sets for supporting the computation of similarities of situation作者: Initiative 時間: 2025-3-24 09:14 作者: 出來 時間: 2025-3-24 12:47
Eyke HüllermeierMajor contribution to the methodical foundations of case-based reasoning.Builds bridges between the fields of CBR and approximate reaoning.First monograph of this type作者: Longitude 時間: 2025-3-24 15:48
Theory and Decision Library Bhttp://image.papertrans.cn/c/image/222308.jpg作者: 有危險 時間: 2025-3-24 22:29 作者: 收養(yǎng) 時間: 2025-3-25 01:24
https://doi.org/10.1007/978-1-4615-0925-7This chapter serves two purposes. Firstly, we provide some background information on similarity-based reasoning and related topics. Secondly, we introduce a formal framework of . (CBI) that provides the basis for the methods which are developed in subsequent chapters.作者: jaundiced 時間: 2025-3-25 03:24 作者: 就職 時間: 2025-3-25 08:56
https://doi.org/10.1007/978-1-4615-0925-7In Chapter 5, it has already been shown that fuzzy rules can be modeled formally as possibility distributions constrained in terms of a combination of the membership functions which define, respectively, their antecedent and consequent part.作者: 不可救藥 時間: 2025-3-25 12:04
Broadband Networks and ServicesEarly work in AI has mainly focused on formal logic as a basis of knowledge representation and has largely rejected approaches from (statistical) decision theory as being intractable and inadequate for expressing the rich structure of (human) knowledge [193].作者: GEAR 時間: 2025-3-25 19:16
Introduction,The idea that reasoning and problem solving (by human beings) are guided by experiences from situations which are similar to the current one has a long tradition in philosophy. It dates back at least to D.作者: Anguish 時間: 2025-3-25 23:30
Similarity and Case-Based Inference,This chapter serves two purposes. Firstly, we provide some background information on similarity-based reasoning and related topics. Secondly, we introduce a formal framework of . (CBI) that provides the basis for the methods which are developed in subsequent chapters.作者: Ganglion-Cyst 時間: 2025-3-26 03:55
Probabilistic Modeling of Case-Based Inference,The main idea of case-based inference is to exploit the information provided by the . of a problem ., .〉 in order to improve the prediction of an unknown outcome . = .(.).作者: OUTRE 時間: 2025-3-26 07:54
Fuzzy Set-Based Modeling of Case-Based Inference II,In Chapter 5, it has already been shown that fuzzy rules can be modeled formally as possibility distributions constrained in terms of a combination of the membership functions which define, respectively, their antecedent and consequent part.作者: Amendment 時間: 2025-3-26 09:42
Case-Based Decision Making,Early work in AI has mainly focused on formal logic as a basis of knowledge representation and has largely rejected approaches from (statistical) decision theory as being intractable and inadequate for expressing the rich structure of (human) knowledge [193].作者: GLIB 時間: 2025-3-26 16:26 作者: Offstage 時間: 2025-3-26 18:40
978-90-481-7431-7Springer Science+Business Media B.V. 2007作者: 揮舞 時間: 2025-3-26 21:07 作者: Conspiracy 時間: 2025-3-27 01:26
Object-Oriented Implementation of a Model for Fuzzy Temporal Reasoning described in the paper. The model is based on the modification of the Petri Nets, called the Petri Nets with Fuzzy Time Tokens (PNFTT). It is suitable for knowledge bases design in intelligent systems that deal with vague, humanlike linguistic expressions.作者: 憤怒事實 時間: 2025-3-27 07:48 作者: BAIT 時間: 2025-3-27 12:00 作者: Inveterate 時間: 2025-3-27 13:44 作者: 方舟 時間: 2025-3-27 20:03
Higher-Order Block Term Decomposition for Spatially Folded fMRI Datanals. In this context, a higher-order Block Term Decomposition (BTD) is applied, for the first time in fMRI analysis. Its effectiveness in handling strong instances of noise is demonstrated via extensive simulation results.作者: 不透明 時間: 2025-3-27 21:58
Yoshiharu Soeta,Yoichi Andoogramming in higher-order logic and A-calculus. This aims at integrating and generalizing declarative programming models such as functional and logic programming. In these two prominent declarative computation models we can view a program as a logical theory and a computation as a deduction.作者: AVOW 時間: 2025-3-28 05:53 作者: 生存環(huán)境 時間: 2025-3-28 06:28 作者: FECK 時間: 2025-3-28 12:09 作者: Cumulus 時間: 2025-3-28 17:19 作者: 清晰 時間: 2025-3-28 20:31
A Preference-Driven Database Approach to Reciprocal User Recommendations in Online Social Networksfriends-of-friends that do not perform well for real life interactions. We demonstrate an integrated database-driven recommendation approach that determines reciprocal user matches, which is an important feature to reduce the risk of rejection. Similarity is computed in a data-adaptive way based on
