36,881 research outputs found
Decision diagrams in machine learning: an empirical study on real-life credit-risk data.
Decision trees are a widely used knowledge representation in machine learning. However, one of their main drawbacks is the inherent replication of isomorphic subtrees, as a result of which the produced classifiers might become too large to be comprehensible by the human experts that have to validate them. Alternatively, decision diagrams, a generalization of decision trees taking on the form of a rooted, acyclic digraph instead of a tree, have occasionally been suggested as a potentially more compact representation. Their application in machine learning has nonetheless been criticized, because the theoretical size advantages of subgraph sharing did not always directly materialize in the relatively scarce reported experiments on real-world data. Therefore, in this paper, starting from a series of rule sets extracted from three real-life credit-scoring data sets, we will empirically assess to what extent decision diagrams are able to provide a compact visual description. Furthermore, we will investigate the practical impact of finding a good attribute ordering on the achieved size savings.Advantages; Classifiers; Credit scoring; Data; Decision; Decision diagrams; Decision trees; Empirical study; Knowledge; Learning; Real life; Representation; Size; Studies;
Gate-Level Simulation of Quantum Circuits
While thousands of experimental physicists and chemists are currently trying
to build scalable quantum computers, it appears that simulation of quantum
computation will be at least as critical as circuit simulation in classical
VLSI design. However, since the work of Richard Feynman in the early 1980s
little progress was made in practical quantum simulation. Most researchers
focused on polynomial-time simulation of restricted types of quantum circuits
that fall short of the full power of quantum computation. Simulating quantum
computing devices and useful quantum algorithms on classical hardware now
requires excessive computational resources, making many important simulation
tasks infeasible. In this work we propose a new technique for gate-level
simulation of quantum circuits which greatly reduces the difficulty and cost of
such simulations. The proposed technique is implemented in a simulation tool
called the Quantum Information Decision Diagram (QuIDD) and evaluated by
simulating Grover's quantum search algorithm. The back-end of our package,
QuIDD Pro, is based on Binary Decision Diagrams, well-known for their ability
to efficiently represent many seemingly intractable combinatorial structures.
This reliance on a well-established area of research allows us to take
advantage of existing software for BDD manipulation and achieve unparalleled
empirical results for quantum simulation
An overview of decision table literature 1982-1995.
This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.
How to Efficiently Handle Complex Values? Implementing Decision Diagrams for Quantum Computing
Quantum computing promises substantial speedups by exploiting quantum
mechanical phenomena such as superposition and entanglement. Corresponding
design methods require efficient means of representation and manipulation of
quantum functionality. In the classical domain, decision diagrams have been
successfully employed as a powerful alternative to straightforward means such
as truth tables. This motivated extensive research on whether decision diagrams
provide similar potential in the quantum domain -- resulting in new types of
decision diagrams capable of substantially reducing the complexity of
representing quantum states and functionality. From an implementation
perspective, many concepts and techniques from the classical domain can be
re-used in order to implement decision diagrams packages for the quantum realm.
However, new problems -- namely how to efficiently handle complex numbers --
arise. In this work, we propose a solution to overcome these problems.
Experimental evaluations confirm that this yields improvements of orders of
magnitude in the runtime needed to create and to utilize these decision
diagrams. The resulting implementation is publicly available as a quantum DD
package at http://iic.jku.at/eda/research/quantum_dd
Inspiring practice: a guide to developing an integrated approach to supervision in children’s trusts
CWDC would like to thank Fran McDonnell and Harry Zutshi for their work on this guide
Inspiring practice: a guide to developing an integrated approach to supervision in children's trusts
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