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Learning, Logic, and Probability: A Unified View [chapter]

Pedro Domingos
2006 Lecture Notes in Computer Science  
uncertainty The Goal  A unified language  Probabilistic graphical models and first-order logic are special cases  Unified inference algorithms  Unified learning algorithms  Easy-to-use software   ...  processes Natural language processing Definite clause grammars Prob. contextfree grammars We Need to Unify the Two  The real world is complex and uncertain  Logic handles complexity  Probability handles  ...   Can we unify resolution and lifted BP?  Can other inference algorithms be lifted?  ... 
doi:10.1007/11874850_2 fatcat:pwtbgmnberfv5apufkkvygpbu4

Learning, Logic, and Probability: A Unified View [chapter]

Pedro Domingos
2006 Lecture Notes in Computer Science  
uncertainty The Goal  A unified language  Probabilistic graphical models and first-order logic are special cases  Unified inference algorithms  Unified learning algorithms  Easy-to-use software   ...  processes Natural language processing Definite clause grammars Prob. contextfree grammars We Need to Unify the Two  The real world is complex and uncertain  Logic handles complexity  Probability handles  ...   Can we unify resolution and lifted BP?  Can other inference algorithms be lifted?  ... 
doi:10.1007/978-3-540-36668-3_1 fatcat:7xyuyldeyrfjdhsriy5rosc3re

Learning, Logic, and Probability: A Unified View [chapter]

Pedro Domingos
2004 Lecture Notes in Computer Science  
uncertainty The Goal  A unified language  Probabilistic graphical models and first-order logic are special cases  Unified inference algorithms  Unified learning algorithms  Easy-to-use software   ...  processes Natural language processing Definite clause grammars Prob. contextfree grammars We Need to Unify the Two  The real world is complex and uncertain  Logic handles complexity  Probability handles  ...   Can we unify resolution and lifted BP?  Can other inference algorithms be lifted?  ... 
doi:10.1007/978-3-540-30109-7_26 fatcat:bcnvefkpizhx5o2ojubolrz6ca

Learning, Logic, and Probability: A Unified View [chapter]

Pedro Domingos
2006 Lecture Notes in Computer Science  
uncertainty The Goal  A unified language  Probabilistic graphical models and first-order logic are special cases  Unified inference algorithms  Unified learning algorithms  Easy-to-use software   ...  processes Natural language processing Definite clause grammars Prob. contextfree grammars We Need to Unify the Two  The real world is complex and uncertain  Logic handles complexity  Probability handles  ...   Can we unify resolution and lifted BP?  Can other inference algorithms be lifted?  ... 
doi:10.1007/11891451_2 fatcat:3lsn5lklcrbavbn2wxo7l7hpfq

Learning, Logic, and Probability: A Unified View [chapter]

Pedro Domingos
2004 Lecture Notes in Computer Science  
Generative Weight Learning  Maximize likelihood or posterior probability  Numerical optimization (gradient or 2 nd order)  No local maxima (i.e., a single global maxima)  Requires inference at each  ...  with Missing DataExp. no. true groundings given observed data Expected no. true groundings given no data x: Observed y: Missing Structure Learning  Start with atomic features  Greedily conjoin features  ... 
doi:10.1007/978-3-540-30215-5_5 fatcat:cbtnzbslbjbqjanumkachaj5dq

Rule Evaluation Measures: A Unifying View [chapter]

Nada Lavrač, Peter Flach, Blaz Zupan
1999 Lecture Notes in Computer Science  
In this paper we develop a unifying view on some of the existing measures for predictive and descriptive induction. We provide a common terminology and notation by means of contingency tables.  ...  rule learning.  ...  This work was partially supported by the Esprit Long Term Research Project 20237 (Inductive Logic Programming 2), a Joint Project with Central/Eastern Europe funded by the Royal Society, and the Slovenian  ... 
doi:10.1007/3-540-48751-4_17 fatcat:izypcagfrjbv3ifgh5wqsfrf5q

Case-Based Recommender Systems: A Unifying View [chapter]

Fabiana Lorenzi, Francesco Ricci
2005 Lecture Notes in Computer Science  
This paper presents a unifying framework to model casebased reasoning recommender systems (CBR-RSs).  ...  CBR-RSs have complex architectures and specialize the CBR problem solving methodology in a number of ways.  ...  A cart represents a meaningful (from the user's point of view) bundling of different products.  ... 
doi:10.1007/11577935_5 fatcat:5iu5bhbc75eovetnaq6fdodwni

Learning, Probability and Logic: Toward a Unified Approach for Content-Based Music Information Retrieval

Helene-Camille Crayencour, Carmine-Emanuele Cella
2019 Frontiers in Digital Humanities  
Traditional approaches have generally treated these two aspects separately, probability and learning being the usual way to represent uncertainty in knowledge, while logical representation being the usual  ...  We advocate that the identified hurdles of current approaches could be overcome by recent developments in the area of Statistical Relational Artificial Intelligence (StarAI) that unifies probability, logic  ...  Intelligence that unifies probability, logic and (deep) learning.  ... 
doi:10.3389/fdigh.2019.00006 fatcat:nsphz2frebgy5bhcybvpfcyfey

Learning, Logic, and Probability: A Unified View [chapter]

Pedro Domingos
Lecture Notes in Computer Science  
uncertainty The Goal  A unified language  Probabilistic graphical models and first-order logic are special cases  Unified inference algorithms  Unified learning algorithms  Easy-to-use software   ...  processes Natural language processing Definite clause grammars Prob. contextfree grammars We Need to Unify the Two  The real world is complex and uncertain  Logic handles complexity  Probability handles  ...   Can we unify resolution and lifted BP?  Can other inference algorithms be lifted?  ... 
doi:10.1007/11801603_1 dblp:conf/pricai/Domingos06 fatcat:btoqo3qgyndzdgd52di3zk43sy

A Unifying Probabilistic View of Associative Learning

Samuel J. Gershman, Jörn Diedrichsen
2015 PLoS Computational Biology  
This article describes a unifying framework encompassing Bayesian and reinforcement learning theories of associative learning.  ...  The goal of this paper is to unify these perspectives and explore the implications of this unification. One set of assumptions about the learning task concerns the target of learning.  ...  Author Contributions Conceived and designed the experiments: SJG. Performed the experiments: SJG. Analyzed the data: SJG. Contributed reagents/materials/analysis tools: SJG. Wrote the paper: SJG.  ... 
doi:10.1371/journal.pcbi.1004567 pmid:26535896 pmcid:PMC4633133 fatcat:efpgngikqfhmrpxdog7i74zgaq

A Unified View of Causal and Non-causal Feature Selection [article]

Kui Yu, Lin Liu, Jiuyong Li
2018 arXiv   pre-print
In this paper, we aim to develop a unified view of causal and non-causal feature selection methods.  ...  With the unified view, we are able to interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods.  ...  In summary, we propose a unified view to bridge the gap in current understanding of the relation between causal and non-causal feature selection methods.  ... 
arXiv:1802.05844v4 fatcat:ntaw52p6zjc55lu3ilcc2mxqm4

A unified view of class-selection with probabilistic classifiers

Hoel Le Capitaine
2014 Pattern Recognition  
In this paper, a unified view of the problem of class-selection with probabilistic classifiers is presented.  ...  It allows to get a relative independence of the performance of a classifier without reject option, and thus a reliable class-selection decision rule evaluation.  ...  The ratio comes from the interpretation of consequent and antecedent in a logical implication. This rule is denoted as LC (for Logical Confidence) in the sequel.  ... 
doi:10.1016/j.patcog.2013.07.020 fatcat:fy3jg2unlvg4taweuo4o6fzv34

Explanation-Based Learning and Reinforcement Learning: A Unified View [chapter]

Thomas G. Dietterich, Nicholas S. Flann
1995 Machine Learning Proceedings 1995  
Barto, Bradtke, and Singh (1995) have observed that many algorithms for reinforcement learning can be viewed as asynchronous dynamic programming.  ...  Most RL methods perform this propagation on a state-by-state basis, while EBL methods compute the weakest preconditions of operators, and hence, perform this propagation on a region-by-region basis.  ...  logic.  ... 
doi:10.1016/b978-1-55860-377-6.50030-x dblp:conf/icml/DietterichF95 fatcat:3p5v5rfeibfj5i2aqepgvyln4u

Endogenous Uncertainty: A Unified View of Market Volatility

Mordecai Kurz
1998 Social Science Research Network  
The theory of Rational Belief Equilibria (RBE) offers a unified paradigm for explaining market volatility by the effect of "Endogenous Uncertainty" on financial markets.  ...  In a belief regime of non-consensus there is a widespread disagreement which cause a balance between sellers and buyers leading to low market volatility.  ...  These frequencies and averages are learned by all agents and represent the "normal" probabilities of events.  ... 
doi:10.2139/ssrn.57947 fatcat:s4xvaqh2ezbotbwwf27ouaukga

The Architecture of Platforms: A Unified View

Carliss Y. Baldwin, C. Jason Woodard
2008 Social Science Research Network  
We conclude by addressing a number of fundamental strategic questions suggested by a unified view of platforms.  ...  Among the most stable elements in a platform architecture are the modular interfaces that mediate between the platform and its complements.  ...  , matrices and layer maps.  ... 
doi:10.2139/ssrn.1265155 fatcat:gq7daclogzhf3fuj7t7udla6gu
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