10,590 research outputs found
The Recommendation Architecture: Lessons from Large-Scale Electronic Systems Applied to Cognition
A fundamental approach of cognitive science is to understand cognitive systems by separating them into modules. Theoretical reasons are described which force any system which learns to perform a complex combination of real time functions into a modular architecture. Constraints on the way modules divide up functionality are also described. The architecture of such systems, including biological systems, is constrained into a form called the recommendation architecture, with a primary separation between clustering and competition. Clustering is a modular hierarchy which manages the interactions between functions on the basis of detection of functionally ambiguous repetition. Change to previously detected repetitions is limited in order to maintain a meaningful, although partially ambiguous context for all modules which make use of the previously defined repetitions. Competition interprets the repetition conditions detected by clustering as a range of alternative behavioural recommendations, and uses consequence feedback to learn to select the most appropriate recommendation. The requirements imposed by functional complexity result in very specific structures and processes which resemble those of brains. The design of an implemented electronic version of the recommendation architecture is described, and it is demonstrated that the system can heuristically define its own functionality, and learn without disrupting earlier learning. The recommendation architecture is compared with a range of alternative cognitive architectural proposals, and the conclusion reached that it has substantial potential both for understanding brains and for designing systems to perform cognitive functions
Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information
The Symbolic Aggregate approXimation (SAX) is a very popular symbolic
dimensionality reduction technique of time series data, as it has several
advantages over other dimensionality reduction techniques. One of its major
advantages is its efficiency, as it uses precomputed distances. The other main
advantage is that in SAX the distance measure defined on the reduced space
lower bounds the distance measure defined on the original space. This enables
SAX to return exact results in query-by-content tasks. Yet SAX has an inherent
drawback, which is its inability to capture segment trend information. Several
researchers have attempted to enhance SAX by proposing modifications to include
trend information. However, this comes at the expense of giving up on one or
more of the advantages of SAX. In this paper we investigate three modifications
of SAX to add trend capturing ability to it. These modifications retain the
same features of SAX in terms of simplicity, efficiency, as well as the exact
results it returns. They are simple procedures based on a different
segmentation of the time series than that used in classic-SAX. We test the
performance of these three modifications on 45 time series datasets of
different sizes, dimensions, and nature, on a classification task and we
compare it to that of classic-SAX. The results we obtained show that one of
these modifications manages to outperform classic-SAX and that another one
slightly gives better results than classic-SAX.Comment: International Conference on Modeling Decisions for Artificial
Intelligence - MDAI 2020: Modeling Decisions for Artificial Intelligence pp
230-23
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Holistic processing of hierarchical structures in connectionist networks
Despite the success of connectionist systems to model some aspects of cognition, critics argue that the lack of symbol processing makes them inadequate for modelling
high-level cognitive tasks which require the representation and processing of hierarchical structures. In this thesis we investigate four mechanisms for encoding hierarchical structures in distributed representations that are suitable for processing in
connectionist systems: Tensor Product Representation, Recursive Auto-Associative
Memory (RAAM), Holographic Reduced Representation (HRR), and Binary Spatter
Code (BSC). In these four schemes representations of hierarchical structures are either
learned in a connectionist network or constructed by means of various mathematical
operations from binary or real-value vectors.It is argued that the resulting representations carry structural information without being themselves syntactically structured. The structural information about a represented
object is encoded in the position of its representation in a high-dimensional representational space. We use Principal Component Analysis and constructivist networks to
show that well-separated clusters consisting of representations for structurally similar
hierarchical objects are formed in the representational spaces of RAAMs and HRRs.
The spatial structure of HRRs and RAAM representations supports the holistic yet
structure-sensitive processing of them. Holistic operations on RAAM representations
can be learned by backpropagation networks. However, holistic operators over HRRs,
Tensor Products, and BSCs have to be constructed by hand, which is not a desirable situation. We propose two new algorithms for learning holistic transformations of HRRs
from examples. These algorithms are able to generalise the acquired knowledge to
hierarchical objects of higher complexity than the training examples. Such generalisations exhibit systematicity of a degree which, to our best knowledge, has not yet been
achieved by any other comparable learning method.Finally, we outline how a number of holistic transformations can be learned in parallel and applied to representations of structurally different objects. The ability to distinguish and perform a number of different structure-sensitive operations is one step
towards a connectionist architecture that is capable of modelling complex high-level
cognitive tasks such as natural language processing and logical inference
Advanced Analysis on Temporal Data
Due to the increase in CPU power and the ever increasing data storage
capabilities, more and more data of all kind is recorded, including temporal
data. Time series, the most prevalent type of temporal data are derived in a
broad number of application domains. Prominent examples include stock price data
in economy, gene expression data in biology, the course of environmental
parameters in meteorology, or data of moving objects recorded by traffic
sensors.
This large amount of raw data can only be analyzed by automated data mining
algorithms in order to generate new knowledge. One of the most basic data
mining operations is the similarity query, which computes a similarity or
distance value for two objects. Two aspects of such an similarity function are
of special interest. First, the semantics of a similarity function and second,
the computational cost for the calculation of a similarity value. The semantics
is the actual similarity notion and is highly dependant on the analysis task at
hand.
This thesis addresses both aspects. We introduce a number of new
similarity measures for time series data and show how they can
efficiently be calculated by means of index structures and query
algorithms.
The first of the new similarity measures is threshold-based. Two
time series are considered as similar, if they exceed a user-given
threshold during similar time intervals. Aside from formally
defining this similarity measure, we show how to represent time
series in such a way that threshold-based queries can be efficiently
calculated. Our representation allows for the specification of the
threshold value at query time. This is for example useful for data
mining task that try to determine crucial thresholds.
The next similarity measure considers a relevant amplitude range.
This range is scanned with a certain resolution and for each
considered amplitude value features are extracted. We consider the
change in the feature values over the amplitude values and thus,
generate so-called feature sequences. Different features can finally
be combined to answer amplitude-level-based similarity queries. In
contrast to traditional approaches which aggregate global feature
values along the time dimension, we capture local characteristics
and monitor their change for different amplitude values.
Furthermore, our method enables the user to specify a relevant range
of amplitude values to be considered and so the similarity notion
can be adapted to the current requirements.
Next, we introduce so-called interval-focused similarity queries. A
user can specify one or several time intervals that should be
considered for the calculation of the similarity value. Our main
focus for this similarity measure was the efficient support of the
corresponding query. In particular we try to avoid loading the
complete time series objects into main memory, if only a relatively
small portion of a time series is of interest. We propose a time
series representation which can be used to calculate upper and
lower distance bounds, so that only a few time series objects have
to be completely loaded and refined. Again, the relevant time
intervals do not have to be known in advance.
Finally, we define a similarity measure for so-called uncertain time series,
where several amplitude values are given for each point in time. This can be
due to multiple recordings or to errors in measurements, so that no exact value
can be specified. We show how to efficiently support queries on uncertain time
series.
The last part of this thesis shows how data mining methods can be used to
discover crucial threshold parameters for the threshold-based similarity
measure. Furthermore we present a data mining tool for time series
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