Graph-Based Representations in Pattern Recognition and Computational Intelligence

Marfil, Rebeca; Escolano, Francisco; Bandera, Antonio

doi:10.1007/978-3-642-02478-8_50

Cited by 8 publications

(6 citation statements)

References 37 publications

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“…In the workplace and in the design of tools used for solving complex tasks, the use of concrete, encodable goals could also serve to encourage engagement with complex tasks. While it is known that data representation in general impacts problem-solving performance (Carroll, Thomas, & Malhotra, 1980;Marfil, Escolano, & Bandera, 2009;Norman, 2002;Noyes & Garland, 2003), the well-definedness of problem goals also clearly impacts performance. This knowledge can be leveraged to improve workplace patterns, by replacing ill-defined goals with well-defined alternatives to better represent complex tasks such that they can be meaningfully encoded in order to result in better engagement.…”

Section: What Are the Implications Of These Findings In General?mentioning

confidence: 99%

The Role of the Goal in Solving Hard Computational Problems: Do People Really Optimize?

Carruthers¹,

Stege²,

Masson³

2018

The Journal of Problem Solving

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The role that the mental, or internal, representation plays when people are solving hard computational problems has largely been overlooked to date, despite the reality that this internal representation drives problem solving. In this work we investigate how performance on versions of two hard computational problems differs based on what internal representations can be generated. Our findings suggest that problem solving performance depends not only on the objective difficulty of the problem, and of course the particular problem instance at hand, but also on how feasible it is to encode the goal of the given problem. A further implication of these findings is that previous human performance studies using NP-hard problems may have, surprisingly, underestimated human performance on instances of problems of this class. We suggest ways to meaningfully frame human performance results on instances of computationally hard problems in terms of these problems' computational complexity, and present a novel framework for interpreting results on problems of this type. The framework takes into account the limitations of the human cognitive system, in particular as it applies to the generation of internal representations of problems of this class.

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Section: What Are the Implications Of These Findings In General?mentioning

confidence: 99%

The Role of the Goal in Solving Hard Computational Problems: Do People Really Optimize?

Carruthers¹,

Stege²,

Masson³

2018

The Journal of Problem Solving

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“…This number k is called the order of the clustering and can be defined a priori or identified at runtime by the algorithm itself. The well known k-means algorithm [2] takes the number of clusters as an argument. It is a two-phase iterative heuristic algorithm that is based on the assignment of each input pattern x i to the cluster with the closest mean i  .…”

Section: A Clustering Structured Data By K-meansmentioning

confidence: 99%

“…However, many interesting applications, coming for instance from computational biology, multimedia intelligent processing and computer vision, deal with structured patterns, such as, images, audio and video sequences, strings and labeled graphs [1], [2]. Usually, in order to take advantage of the existing data driven modeling systems, each pattern of a structured domain X is reduced to a set of real valued features by adopting a preprocessing function, tailored on the specific application.…”

Section: Introductionmentioning

confidence: 99%

On the Problem of Modeling Structured Data with the MinSOD Representative

Vescovo¹,

Livi²,

Massimo³

et al. 2014

IJCTE

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Abstract-The possibility to face pattern recognition problems directly on structured domains (e.g., multimedia data, strings, graphs) is fundamental to the effective solution of many interesting applications. In this paper, we deal with a clustering problem defined in the string domain, focusing on the problem of cluster representation in data domains where only a dissimilarity measure can be fixed. To this aim, we adopt the MinSOD (Minimum Sum of Distances) cluster representation technique, which defines the representative as the element of the cluster minimizing the sum of dissimilarities from all the other elements in the considered set. Since the precise computation of the MinSOD have a high computational cost, we propose a suboptimal procedure consisting in computing the representative of the cluster considering only a reduced pool of samples, instead of the whole set of objects in the cluster. We have carried out some tests in order to ascertain the sensitivity of the clustering procedure with respect to the number of samples in the pool used to compute the MinSOD. Results show a good robustness of the proposed procedure. The implementations are available as part of the SPARE library, which is available as an open source project.

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“…Representation of problem input impacts problem-solving performance in a number of different domains, both in terms of external representation (Marfil et al, 2009) and internal representation (Newell & Simon, 1972). While internal representation impacts performance, as seen in expert chess playing, it is of interest to better understand what determines, for example, which internal representation(s) are used on a given problem, and what limits which internal representations are available to different kinds of problem solvers or for different kinds of problems.…”

mentioning

confidence: 99%

“…Advancements in this area are impressive, and have achieved remarkable results, but deeper examination reveals a rather simple level of collaboration between humans and computers. We are still far away from the kind of advanced collaborative problem solving by human and machine that would be needed to push the limits of problem solving as we know it (Marfil, Escolano, & Bandera, 2009). To harness the combined (computing and cognitive) power of both machine and human, it is essential to understand the power and limitations of both machines and humans.…”

mentioning

confidence: 99%