79,758 research outputs found
Identifying hidden contexts
In this study we investigate how to identify hidden contexts from the data in classification tasks.
Contexts are artifacts in the data, which do not predict the class label directly.
For instance, in speech recognition task speakers might have different accents, which do not directly discriminate between the spoken words.
Identifying hidden contexts is considered as data preprocessing task, which can help to build more accurate classifiers, tailored for particular contexts and give an insight into the data structure.
We present three techniques to identify hidden contexts, which hide class label information from the input data and partition it using clustering techniques.
We form a collection of performance measures to ensure that the resulting contexts are valid.
We evaluate the performance of the proposed techniques on thirty real datasets.
We present a case study illustrating how the identified contexts can be used to build specialized more accurate classifiers
Certified Algorithms: Worst-Case Analysis and Beyond
In this paper, we introduce the notion of a certified algorithm. Certified algorithms provide worst-case and beyond-worst-case performance guarantees. First, a ?-certified algorithm is also a ?-approximation algorithm - it finds a ?-approximation no matter what the input is. Second, it exactly solves ?-perturbation-resilient instances (?-perturbation-resilient instances model real-life instances). Additionally, certified algorithms have a number of other desirable properties: they solve both maximization and minimization versions of a problem (e.g. Max Cut and Min Uncut), solve weakly perturbation-resilient instances, and solve optimization problems with hard constraints.
In the paper, we define certified algorithms, describe their properties, present a framework for designing certified algorithms, provide examples of certified algorithms for Max Cut/Min Uncut, Minimum Multiway Cut, k-medians and k-means. We also present some negative results
A Study of NK Landscapes' Basins and Local Optima Networks
We propose a network characterization of combinatorial fitness landscapes by
adapting the notion of inherent networks proposed for energy surfaces (Doye,
2002). We use the well-known family of landscapes as an example. In our
case the inherent network is the graph where the vertices are all the local
maxima and edges mean basin adjacency between two maxima. We exhaustively
extract such networks on representative small NK landscape instances, and show
that they are 'small-worlds'. However, the maxima graphs are not random, since
their clustering coefficients are much larger than those of corresponding
random graphs. Furthermore, the degree distributions are close to exponential
instead of Poissonian. We also describe the nature of the basins of attraction
and their relationship with the local maxima network.Comment: best paper nominatio
Clustering in Hilbert space of a quantum optimization problem
The solution space of many classical optimization problems breaks up into
clusters which are extensively distant from one another in the Hamming metric.
Here, we show that an analogous quantum clustering phenomenon takes place in
the ground state subspace of a certain quantum optimization problem. This
involves extending the notion of clustering to Hilbert space, where the
classical Hamming distance is not immediately useful. Quantum clusters
correspond to macroscopically distinct subspaces of the full quantum ground
state space which grow with the system size. We explicitly demonstrate that
such clusters arise in the solution space of random quantum satisfiability
(3-QSAT) at its satisfiability transition. We estimate both the number of these
clusters and their internal entropy. The former are given by the number of
hardcore dimer coverings of the core of the interaction graph, while the latter
is related to the underconstrained degrees of freedom not touched by the
dimers. We additionally provide new numerical evidence suggesting that the
3-QSAT satisfiability transition may coincide with the product satisfiability
transition, which would imply the absence of an intermediate entangled
satisfiable phase.Comment: 11 pages, 6 figure
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