New ideas for brain modelling 3

Greer, Kieran

doi:10.1016/j.cogsys.2018.12.016

Cited by 19 publications

(51 citation statements)

References 52 publications

(95 reference statements)

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“…The Concept Base has been used both for a cognitive application and as a database. For the cognitive model ( [4] and related papers), the descriptive layers might actually fit better with the symbolic neural network. This is the upper structure that is more experience-based and would process dynamic context more naturally than the knowledge-based concept tree.…”

Section: Discussionmentioning

confidence: 99%

Adding context to Concept Trees

Greer

2019

IJISDC

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Concept Trees are a type of database that can organise arbitrary textual information using a very simple rule. Each tree ideally represents a single cohesive concept and the trees can link with each other for navigation and semantic purposes. The trees are therefore a type of semantic network and would benefit from having a consistent level of context for each of the nodes. The tree nodes have a mathematical basis allowing for a consistent build process. These would represent nouns or verbs in a text sentence, for example. A basic test on text documents shows that the tree structure could be inherent in natural language. New to the design can then be lists of descriptive elements for each of the nodes. The descriptors can also be weighted, but do not have to follow the strict counting rule of the tree nodes. With the new descriptive layers, a much richer type of knowledge can be achieved and a consistent method for adding context is suggested. It is also suggested to use the linking structure of the licas system as a basis for the context links. The mathematical model is extended further and to finish, a query language is suggested for practical applications.

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Section: Discussionmentioning

confidence: 99%

Adding context to Concept Trees

Greer

2019

IJISDC

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“…There is clearly a problem with the design that prevents it being a universal classifier. The new classifier may also be of interest with respect to biological models of the human brain, which will be elaborated on later, but papers that note the importance of neuron links in the brain for communication processes include [19] [36]. Some of the health datasets have been tested, where a summary of the current state there can be found in [26].…”

Section: Related Workmentioning

confidence: 99%

Category Trees - Classifiers that Branch on Category

Greer¹

2021

IJAIA

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This paper presents a batch classifier that splits a dataset into tree branches depending on the category type. It has been improved from the earlier version and fixed a mistake in the earlier paper. Two important changes have been made. The first is to represent each category with a separate classifier. Each classifier then classifies its own subset of data rows, using batch input values to create the centroid and also represent the category itself. If the classifier contains data from more than one category however, it needs to create new classifiers for the incorrect data. The second change therefore is to allow the classifier to branch to new layers when there is a split in the data, and create new classifiers there for the data rows that are incorrectly classified. Each layer can therefore branch like a tree - not for distinguishing features, but for distinguishing categories. The paper then suggests a further innovation, which is to represent some data columns with fixed value ranges, or bands. When considering features, it is shown that some of the data can be classified directly through fixed value ranges, while the rest must be classified using a classifier technique and the idea allows the paper to discuss a biological analogy with neurons and neuron links. Tests show that the method can successfully classify a diverse set of benchmark datasets to better than the state-of-the-art.

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“…A relatively new version called TopoART [19] is able to address this problem, as the shapes of the clusters do not depend on the order of creation of the associated categories. The Frequency Grid algorithm [15] that is used later, also suffers from this problem, where an improvement is suggested in section 4.2.1.…”

Section: Related Workmentioning

confidence: 99%

“…With regard to the unsupervised part, the author has published some related papers. The ensemblehierarchy model has close relations to one suggested for the cognitive model [14]- [16]. The Self-Organising Map [18] is obviously of interest, or SOM with extensions [5], as are the clustering algorithms that consider sets of closest nodes, such as DBSCAN [10], kNN or k-Means [20].…”

Section: Related Workmentioning

confidence: 99%

“…This paper describes a design that can be used for Explainable AI. It can probably be integrated into an existing cognitive model [14] at the boundary between the middle and upper levels, where the knowledge-based and the experienced-based systems share information. The lower level is a nested ensemble of patterns created by selforganisation.…”

Section: Introductionmentioning

confidence: 99%

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A Pattern-Hierarchy Classifier for Reduced Teaching

Greer

2020

Wseas Transactions on Computers

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This paper describes a design that can be used for Explainable AI. The lower level is a nested ensemble of patterns created by self-organisation. The upper level is a hierarchical tree, where nodes are linked through individual concepts, so there is a transition from mixed ensemble masses to specific categories. Lower-level pattern ensembles are learned in an unsupervised manner and then split into branches when it is clear that the category has changed. Links between the two levels define that the concepts are learned and missing links define that they are guessed only. This paper proposes some new clustering algorithms for producing the pattern ensembles, that are themselves an ensemble which converges through aggregations. Multiple solutions are also combined, to make the final result more robust. One measure of success is how coherent these ensembles are, which means that every data row in the cluster belongs to the same category. The total number of clusters is also important and the teaching phase can correct the ensemble estimates with respect to both of these. A teaching phase would then help the classifier to learn the true category for each input row. During this phase, any classifier can learn or infer correct classifications from some other classifier's knowledge, thereby reducing the required number of presentations. As the information is added, cross-referencing between the two structures allows it to be used more widely, where a unique structure can build up that would not be possible by either method separately.

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New ideas for brain modelling 3

Cited by 19 publications

References 52 publications

Adding context to Concept Trees

Adding context to Concept Trees

Category Trees - Classifiers that Branch on Category

A Pattern-Hierarchy Classifier for Reduced Teaching

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