1,342 research outputs found
Learning First-Order Definitions of Functions
First-order learning involves finding a clause-form definition of a relation
from examples of the relation and relevant background information. In this
paper, a particular first-order learning system is modified to customize it for
finding definitions of functional relations. This restriction leads to faster
learning times and, in some cases, to definitions that have higher predictive
accuracy. Other first-order learning systems might benefit from similar
specialization.Comment: See http://www.jair.org/ for any accompanying file
CLP-based protein fragment assembly
The paper investigates a novel approach, based on Constraint Logic
Programming (CLP), to predict the 3D conformation of a protein via fragments
assembly. The fragments are extracted by a preprocessor-also developed for this
work- from a database of known protein structures that clusters and classifies
the fragments according to similarity and frequency. The problem of assembling
fragments into a complete conformation is mapped to a constraint solving
problem and solved using CLP. The constraint-based model uses a medium
discretization degree Ca-side chain centroid protein model that offers
efficiency and a good approximation for space filling. The approach adapts
existing energy models to the protein representation used and applies a large
neighboring search strategy. The results shows the feasibility and efficiency
of the method. The declarative nature of the solution allows to include future
extensions, e.g., different size fragments for better accuracy.Comment: special issue dedicated to ICLP 201
SkILL - a Stochastic Inductive Logic Learner
Probabilistic Inductive Logic Programming (PILP) is a rel- atively unexplored
area of Statistical Relational Learning which extends classic Inductive Logic
Programming (ILP). This work introduces SkILL, a Stochastic Inductive Logic
Learner, which takes probabilistic annotated data and produces First Order
Logic theories. Data in several domains such as medicine and bioinformatics
have an inherent degree of uncer- tainty, that can be used to produce models
closer to reality. SkILL can not only use this type of probabilistic data to
extract non-trivial knowl- edge from databases, but it also addresses
efficiency issues by introducing a novel, efficient and effective search
strategy to guide the search in PILP environments. The capabilities of SkILL
are demonstrated in three dif- ferent datasets: (i) a synthetic toy example
used to validate the system, (ii) a probabilistic adaptation of a well-known
biological metabolism ap- plication, and (iii) a real world medical dataset in
the breast cancer domain. Results show that SkILL can perform as well as a
deterministic ILP learner, while also being able to incorporate probabilistic
knowledge that would otherwise not be considered
Efficient Learning and Evaluation of Complex Concepts in Inductive Logic Programming
Inductive Logic Programming (ILP) is a subfield of Machine Learning with foundations in logic
programming. In ILP, logic programming, a subset of first-order logic, is used as a uniform
representation language for the problem specification and induced theories. ILP has been
successfully applied to many real-world problems, especially in the biological domain (e.g. drug
design, protein structure prediction), where relational information is of particular importance.
The expressiveness of logic programs grants flexibility in specifying the learning task and understandability
to the induced theories. However, this flexibility comes at a high computational
cost, constraining the applicability of ILP systems. Constructing and evaluating complex concepts
remain two of the main issues that prevent ILP systems from tackling many learning
problems. These learning problems are interesting both from a research perspective, as they
raise the standards for ILP systems, and from an application perspective, where these target
concepts naturally occur in many real-world applications. Such complex concepts cannot
be constructed or evaluated by parallelizing existing top-down ILP systems or improving the
underlying Prolog engine. Novel search strategies and cover algorithms are needed.
The main focus of this thesis is on how to efficiently construct and evaluate complex hypotheses
in an ILP setting. In order to construct such hypotheses we investigate two approaches.
The first, the Top Directed Hypothesis Derivation framework, implemented in the ILP system
TopLog, involves the use of a top theory to constrain the hypothesis space. In the second approach
we revisit the bottom-up search strategy of Golem, lifting its restriction on determinate
clauses which had rendered Golem inapplicable to many key areas. These developments led to
the bottom-up ILP system ProGolem. A challenge that arises with a bottom-up approach is the
coverage computation of long, non-determinate, clauses. Prolog’s SLD-resolution is no longer
adequate. We developed a new, Prolog-based, theta-subsumption engine which is significantly
more efficient than SLD-resolution in computing the coverage of such complex clauses.
We provide evidence that ProGolem achieves the goal of learning complex concepts by presenting
a protein-hexose binding prediction application. The theory ProGolem induced has
a statistically significant better predictive accuracy than that of other learners. More importantly,
the biological insights ProGolem’s theory provided were judged by domain experts to
be relevant and, in some cases, novel
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