This paper presents a methodology to design a discrete-event system (DES) for the on-line supervision of biotechnological process. The DES is synthesised applying Wavelet Transform and inductive logic programming on t...
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ISBN:
(纸本)0780367227
This paper presents a methodology to design a discrete-event system (DES) for the on-line supervision of biotechnological process. The DES is synthesised applying Wavelet Transform and inductive logic programming on the measured signals constrained to the biotechnologist expert validation.
In this paper, we propose an inductive logic programming learning method which aims at automatically extracting special Noun-Verb (N-V) pairs from a corpus in order to build up semantic lexicons based on Pustejovsky&#...
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In this paper, we propose an inductive logic programming learning method which aims at automatically extracting special Noun-Verb (N-V) pairs from a corpus in order to build up semantic lexicons based on Pustejovsky's Generative Lexicon (GL) principles (Pustejovsky, 1995). In one of the components of this lexical model, called the qualia structure, words are described in terms of semantic roles. For example, the telic role indicates the purpose or function of an item (cut for knife), the agentive role its creation mode (build for house), etc. The qualia structure of a noun is mainly made up of verbal associations, encoding relational information. The inductive logic programming learning method that we have developed enables us to automatically extract from a corpus N-V pairs whose elements are linked by one of the semantic relations defined in the qualia structure in GL, and to distinguish them, in terms of surrounding categorial context from N-V pairs also present in sentences of the corpus but not relevant. This method has been theoretically and empirically validated, on a technical corpus. The N-V pairs that have been extracted will further be used in information retrieval applications for index expansion.
This paper describes an inductive logic programming learning method designed to acquire from a corpus specific Noun-Verb (N-V) pairs-relevant in information retrieval applications to perform index expansion-in order t...
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This paper describes an inductive logic programming learning method designed to acquire from a corpus specific Noun-Verb (N-V) pairs-relevant in information retrieval applications to perform index expansion-in order to build up semantic lexicons based on Pustejovsky's generative lexicon (GL) principles (Pustejovsky, 1995). In one of the components of this lexical model, called the qualia structure, words are described in terms of semantic roles. For example, the telic role indicates the purpose or function of an item (cut for knife), the agentive role its creation mode (build for house), etc. The qualia structure of a noun is mainly made up of verbal associations, encoding relational information. The learning method enables us to automatically extract, from a morpho-syntactically and semantically tagged corpus, N-V pairs whose elements are linked by one of the semantic relations defined in the qualia structure in GL. It also infers rules explaining what in the surrounding context distinguishes such pairs from others also found in sentences of the corpus but which are not relevant. Stress is put here on the learning efficiency that is required to be able to deal with all the available contextual information, and to produce linguistically meaningful rules.
inductive logic programming (ILP) deals with the problem of finding a hypothesis covering given positive examples and excluding negative examples. It is a subfield of machine learning that uses first-order logic as a ...
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ISBN:
(纸本)9780769545967
inductive logic programming (ILP) deals with the problem of finding a hypothesis covering given positive examples and excluding negative examples. It is a subfield of machine learning that uses first-order logic as a uniform representation for examples and hypothesis. In this paper we propose a method to boost given ILP learning algorithm by first decomposing the set of examples to subsets and applying the learning algorithm to each subset separately, second, merging the hypotheses obtained for subsets to get a single hypothesis for the complete set of examples, and finally refining this single hypothesis to make it shorter.
This work presents some approaches to overcome current Reinforcement Learning limits. We implement a simple virtual environment and some state-of-the-art Reinforcement Learning algorithms for testing and producing a b...
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ISBN:
(纸本)9783030456900;9783030456917
This work presents some approaches to overcome current Reinforcement Learning limits. We implement a simple virtual environment and some state-of-the-art Reinforcement Learning algorithms for testing and producing a baseline for comparison. Then we implement a Relational Reinforcement Learning algorithm that shows superior performance to the baseline but requires introducing human knowledge. We also propose that Model-based Reinforcement Learning can help us overcome some of the barriers. For better World models, we explore inductive logic programming methods, such as First-Order inductive Learner, and develop an improved version of it, more adequate to Reinforcement Learning environments. Finally we develop a novel Neural Network architecture, the inductivelogic Neural Network, to fill the gaps of the previous implementations, that shows great promise.
This paper describes LPMEME, a new learning algorithm for inductive logic programming that uses statistical techniques to find first-order patterns. LPMEME takes as input examples in the form of logical facts and outp...
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We present a novel approach to cluster sets of protein sequences, based on inductive logic programming (ILP). Preliminary results show that;the method proposed Produces understand able descriptions/explanations of the...
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ISBN:
(纸本)9783642024801
We present a novel approach to cluster sets of protein sequences, based on inductive logic programming (ILP). Preliminary results show that;the method proposed Produces understand able descriptions/explanations of the clusters. Furthermore, it can be used as a knowledge elicitation tool to explain clusters proposed by other clustering approaches, such as standard phylogenetic programs.
Effectiveness and efficiency are two most important properties of ILP approaches. For both top-down and bottom-up search-based approaches, greater efficiency is usually gained at the expense of effectiveness. In this ...
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ISBN:
(纸本)9783540696087
Effectiveness and efficiency are two most important properties of ILP approaches. For both top-down and bottom-up search-based approaches, greater efficiency is usually gained at the expense of effectiveness. In this paper, we propose a bottom-up approach, called ILP by instance patterns, for the problem of concept learning in ILP. This approach is based on the observation that each example has its own pieces of description in the background knowledge, and the example together with these descriptions constitute a instance of the concept subject to learn. Our approach first captures the instance structures by patterns, then constructs the final theory purely from the patterns. On the effectiveness aspect, this approach does not assume determinacy of the learned concept. On the efficiency aspect, this approach is more efficient than existing ones due to its constructive nature, the fact that after the patterns are obtained, both the background and examples are not needed anymore, and the fact that it does not perform coverage test and needs no theorem prover.
inductive logic programming is a discipline investigating invention of clausal theories from observed examples such that for given evidence and background knowledge we are finding a hypothesis covering all positive ex...
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inductive logic programming is a discipline investigating invention of clausal theories from observed examples such that for given evidence and background knowledge we are finding a hypothesis covering all positive examples and excluding all negative ones. In this thesis we extend an existing work on template consistency to general consistency. We present a three-phase algorithm DeMeR decomposing the original problem into smaller subtasks, merging all subsolutions together yielding a complete solution and finally refining the result in order to get a compact final hypothesis. Furthermore, we focus on a method how each individual subtask is solved and we introduce a generate-and-test method based on the probabilistic history-driven approach for this purpose. We analyze each stage of the proposed algorithms and demonstrate its impact on a runtime and a hypothesis structure. In particular, we show that the first phase of the algorithm concentrates on solving the problem quickly at the cost of longer solutions whereas the other phases refine these solutions into an admissible form. Finally, we prove that our technique outperforms other algorithms by comparing its results for identifying common structures in random graphs to existing systems.
Background: There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is inductivelogic ...
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Background: There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is inductive logic programming (ILP), which automatically generates comprehensible rules in addition to prediction. The development of ILP systems which can learn rules of the complexity required for studies on protein structure remains a challenge. In this work we use a new ILP system, ProGolem, and demonstrate its performance on learning features of hexose-protein interactions. Results: The rules induced by ProGolem detect interactions mediated by aromatics and by planar-polar residues, in addition to less common features such as the aromatic sandwich. The rules also reveal a previously unreported dependency for residues CYS and LEU. They also specify interactions involving aromatic and hydrogen bonding residues. This paper shows that inductive logic programming implemented in ProGolem can derive rules giving structural features of protein/ligand interactions. Several of these rules are consistent with descriptions in the literature. Conclusions: In addition to confirming literature results, ProGolem's model has a 10-fold cross-validated predictive accuracy that is superior, at the 95% confidence level, to another ILP system previously used to study protein/hexose interactions and is comparable with state-of-the-art statistical learners.
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