2009
DOI: 10.1007/978-3-642-02478-8_50
|View full text |Cite
|
Sign up to set email alerts
|

Graph-Based Representations in Pattern Recognition and Computational Intelligence

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
6
0

Year Published

2011
2011
2018
2018

Publication Types

Select...
4
2
2

Relationship

0
8

Authors

Journals

citations
Cited by 8 publications
(6 citation statements)
references
References 37 publications
0
6
0
Order By: Relevance
“…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%
“…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%
“…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%
“…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%