nils-herrmann/clean_scirepeval huggingface dataset:
Kernel Maximum Likelihood Hebbian Learning

We present a novel method based on a recently proposed extension to a negative feedback network which uses simple Hebbian learning to self-organise called Maximum Likelihood Hebbian learning . We use the kernel version of the ML algorithm on data from a spectroscopic analysis of a stained glass rose window in a Spanish cathedral. It is hoped that in classifying the origin and date of each segment it will help in the restoration of this and other historical stain glass windows.
A Cooperative Unsupervised Architecture to Develop a Business Management Model

This paper describes ongoing multidisciplinary research which aims to analyse an to apply neural networks architectures to the emerging field of Knowledge Management. In this case, we illustrate the particular use of a novel connectionist architecture, characterised for its ability to capture some type of topological ordering. It has been used to build a part of a Global and Integral Model of Business Management, which provides a global improvement in the firm, in terms of flexibility, value and competitiveness. In this paper we approach knowledge management from a theoretical and practical point of view, showing the repercussions that their transformations from the inferior states (data and information) to the highest ones (knowledge and their management) have in the individual and organizational responsibilities.
An Intelligent Aircraft Landing Support System

We discuss the problem of continuing accidents in contemporary aircraft landings and propose three autonomous agents whose task it is to jointly monitor the aircraft and its flight crew. Two of these agents are monitoring the path of the aircraft, one armed with prior knowledge of how planes tend to land at that airport, the other with the ability to project forward from the plane’s current position in order to identify potential dangers. The third agent monitors the flight crew’s behavior. These three agents act together to improve safety in the specific process of landing the aircraft.
A Connectionist Knowledge Management Model

In this paper, we illustrate the particular use of a novel connectionist architecture, characterised for its ability to capture some type of topological ordering. It has been used to build a part of a Global and Integral Model of Business Management, which provides a global improvement in the firm, in terms of flexibility, value and competitiveness. In this paper we approach knowledge management from a theoretical and practical point of view, showing the repercussions that their transformations from the inferior states (data and information) to the highest ones (knowledge and their management) have in the individual and organizational responsibilities.
Non-linear principal components: projection and reconstruction

We review a negative feedback implementation of a Principal Component Analysis artificial neural network and show how, by decoupling the feedforward and feedback mechanism, we may separately affect the projection and reconstruction stages of the network. We therefore introduce a nonlinearity into the projection stage and compare the resulting mapping with a mixture of linear principal components. Finally we derive learning rules which are more optimal for different types of noise and illustrate the resulting network's greater stability on an artificial data set corrupted by shot noise.
Unsupervised Extraction of Structural Information from High Dimensional Visual Data

We present three unsupervised artificial neural networks for the extraction of structural information from visual data. The ability of each network to represent structured knowledge in a manner easily accessible to human interpretation is illustrated using artificial visual data. These networks are used to collectively demonstrate a variety of unsupervised methods for identifying features in visual data and the structural representation of these features in terms of orientation, temporal and topographical ordering, and stereo disparity.
A GIS AND WEB-BASED DECISION SUPPORT TOOL FOR THE MANAGEMENT OF URBAN SOILS

Soils are fundamental to urban environmental quality. They have aesthetic and recreational functions in parks and gardens and contribute to the preservation of biodiversity. They also directly influence citizens' health. This paper describes the initial development of a decision support tool (DST) to appraise the urban soil resource and its valuable functions within a wide range of urban environments. It is aimed to provide assistance to local, national, and European authorities in the optimum planning and sustainable management of their soils resources. The DST is an integrated information technology system based on a spatially distributed environmental database management system of soil quality parameters and soil characteristics together with broader environmental factors. The integration is achieved through the implementation of the DST within the framework of a geographical information system coupled with risk assessment, pollution decay, and other models including the handling of “soft” data. The DST will be web-based, allowing wide access to all stakeholders and citizens, providing on-line forums for consultations, and exchange of information and feedback among stakeholders. It is designed to support true transparency in decision making at the local and international levels. The paper focuses specifically on the development of the system architecture of the DST.
A Kernel Method for Classification

Kernel Maximum Likelihood Hebbian Learning Scale Invariant Maps is a novel technique developed to facilitate the clustering of complex data effectively and efficiently and that is characterised for converging remarkably quickly. The combination of Maximum Likelihood Hebbian Learning Scale Invariant Map and the Kernel Space provides a very smooth scale invariant quantisation which can be used as a clustering technique. The efficiency of this method have been used to analyse an oceanographic problem.
Non-linear data structure extraction using simple hebbian networks

We present a class a neural networks algorithms based on simple hebbian learning which allow the finding of higher order structure in data. The neural networks use negative feedback of activation to selforganise; such networks have previously been shown to be capable of performing principal component analysis (PCA). In this paper, this is extended to exploratory projection pursuit (EPP), which is a statistical method for investigating structure in high-dimensional data sets. As opposed to previous proposals for networks which learn using hebbian learning, no explicit weight normalisation, decay or weight clipping is required. The results are extended to multiple units and related to both the statistical literature on EPP and the neural network literature on non-linear PCA.
Strategy Selection in Games Using Co-evolution Between Artificial Immune Systems

In this paper, we create a simple artificial computer game in order to illustrate a means of making software players in computer games more intelligent. Despite being known as AIs, the current generation of computer players are anything but intelligent. We suggest an algorithm motivated by concepts from Artificial Immune Systems (AIS) in which an attack from one opponent is met with a response from the other which is refined in time to create an optimal response to that attack.. We refine the AIS algorithm in that we model the response with a probability vector rather than a population of antibodies. Some typical results are shown on the simple computer game.
Initialising Self-Organising Maps

We review a technique for creating Self-0rganising Maps (SOMs) in a Feature space which is nonlinearly related to the original data space. We show that convergence is remarkably fast for this method. By considering the linear feature space, we show that it is the interaction between the overcomplete basis in which learning takes place and the mixture of one-shot and incremental learning which comprises the method that gives the method its power. We illustrate the method on real and artificial data sets.
Bagging and Bumping Self Organising Maps

In this paper, we apply the combination method of bagging which has been developed in the context of supervised learning of classifiers and regressors to the unsupervised artificial neural network known as the Self Organising Map. We show that various initialisation techniques can be used to create maps which are comparable by humans by eye. We then use a semi-supervised version of the SOM to classify data sets and show how bagging may be used to improve classification. We then compare bumping and bagging on this data set.
An Extension of epsilon-Insensitive Hebbian Learning to Form a Non-Interfering Basis

We review an extension of Hebbian learning which has been called e-Insensitive Hebbian Learning (Fyfe and MacDonald, 2001) and derive lateral connections from a probability density function. We use these lateral connections to move outputs towards the mode of the pdf and use the resulting outputs to train the feedforward connections. We show that the resulting network is able to identify a single orientation of bars from a mixture of horizontal and vertical bars.
Connectionist Techniques For The Identification And Suppression Of Interfering Underlying Factors

We consider the difficult problem of identification of independent causes from a mixture of them when these causes interfere with one another in a particular manner: those considered are visual inputs to a neural network system which are created by independent underlying causes which may occlude each other. The prototypical problem in this area is a mixture of horizontal and vertical bars in which each horizontal bar interferes with the representation of each vertical bar and vice versa. Previous researchers have developed artificial neural networks which can identify the individual causes; we seek to go further in that we create artificial neural networks which identify all the horizontal bars from only such a mixture. This task is a necessary precursor to the development of the concept of "horizontal" or "vertical".
Structuring global responses of local filters using lateral connections

This paper reviews an unsupervised artificial neural network that has been shown to perform principal component analysis and a constrained version of the same network that has been shown to perform factor analysis. It is shown that this network, when trained on real video data, finds filters that are both local in time and local in space. It is further shown that the type of movement and environment in these video sequences determines the shape of the filters found. It is then shown that lateral connections derived from finding the mode of a probability density function can be used to form a global ordering of the output responses of the network and that different parameter regimes can be used to differentiate between competing, mutually interfering classes of factors. Indeed, some parameter values can be used to suppress entirely particular classes of factors. The net effect on video data is that specific types of movement can be identified by examining the network's outputs’ responses.
Relevance and Kernel Self-Organising Maps

We review the recently proposed method of Kernel Self-organising maps (KSOM) which has been shown to exhibit very fast convergence. We show that this is due to an interaction between the fact that we are working in an overcomplete basis and the fact that we are using a mixture of one-shot and incremental learning. We then review Relevance Vector Machines which is a supervised training method related to Support Vector Machines and apply it to creating the Relevance Self-Organising Map, RSOM. We show results on artificial data and on the iris data set.
Progressive Concept Formation in Self-Organising Maps

We present a new neural model, which extends the classical competitive learning (CL) by performing a Principal Components Analysis (PCA) at each neuron. This model represents an improvement with respect to known local PCA rnethods, because it is not needed to present the entire data set to the network on each computing step. This allows a fast execution, while retaining the climensionality reduction properties of the PCA. Furthermore, every neuron is able to modify its behaviour to adapt to the local dimensionality of the input distribution. Hence our model has a dimensionality estimation capability. Experimental results are presented, which show the dimensionality reduction capabilities of the model with multisensor images.
Maximum Likelihood Hebbian Learning Based Retrieval Method for CBR Systems

CBR systems are normally used to assist experts in the resolution of problems. During the last few years researchers have been working in the development of techniques to automate the reasoning stages identified in this methodology. This paper presents a Maximum Likelihood Hebbian Learning-based method that automates the organisation of cases and the retrieval stage of case-based reasoning systems. The proposed methodology has been derived as an extension of the Principal Component Analysis, and groups similar cases, identifying clusters automatically in a data set in an unsupervised mode. The method has been successfully used to completely automate the reasoning process of an oceanographic forecasting system and to improve its performance.
Complexity Pursuit for Financial Prediction

We compare pre-processing time series data using Complexity Pursuit (CP) 2 and Logarithm Complexity Pursuit (LCP)  with a view to subsequently using a multi-layer perceptron (MLP) to forecast on the data set. Our rationale  is that forecasting the underlying factors will be easier than forecasting the original time series which is a combination of these factors. The projections of the data onto the filters found by the pre-processing method were fed into the MLP and it was trained to find Least Mean Square Error (LMSE). Both methods find interesting structure in the time series but LCP is more robust and achieves the best (in terms of least mean square error) performance.
Extraction of visual information using maximum likelihood Hebbian learning

We explore an extension of Hebbian learning which has been called /spl epsiv/-insensitive Hebbian learning, and derive lateral connections from a probability density function (PDF). We use these lateral connections to move outputs towards the mode of the PDF and use the resulting outputs to train the feedforward connections. We show that /spl epsiv/-insensitive Hebbian learning may be considered as a special case of maximum likelihood Hebbian learning and investigate the resulting network with both real and artificial data. We finally show that the resulting network is able to identify motion in the environment.
Maximum and minimum likelihood Hebbian rules for exploratory projection pursuit

We develop an algorithm which identifies a low dimensional basis of high dimensional data in such a way that the interesting structure in the high dimensional data is optimally preserved. We do this by extending a principal component analysis network so that instead of minimising mean squared error, the network minimises other functions of the error between the projections of the data and the original data set. We do this by considering the residuals from the network in a probabilistic perspective and show that the original PCA network is optimal for Gaussian data. We relate the new rules to the statistical method of exploratory projection pursuit and show it working on both real and artificial data.
Extraction of Independent Causes Using Lateral Connections

This paper is an exploration of a straightforward but powerful unsupervised artificial neural network technique for the efficient coding of artificial and real data. Using an energy function based on the Rectified Gaussian distribution, we derive a neural architecture based on an unsupervised negative feedback network with fixed lateral connections. We show that, not only can this network identify local correlated structure in visual data but through the use of appropriate lateral connections, we can obtain a grouping of similar causes on the output response of the network. We show that the network may be used to form local spatiotemporal filters in response to real images contained in video. The shape of these filters reflects the nature of the video sequences.
Multi-stream Exploratory Projection Pursuit for the Formation of Complex Cells Similar to Visual Cortical Neurons

A multi-stream extension of Exploratory Projection Pursuit is proposed as a method for the formation of local, spatiotemporal, oriented filters similar to complex cells found in the visual cortex. This algorithm, which we call the Exploratory Correlation Analysis (ECA) network, is derived to maximise dependencies between separate, but related, data streams. By altering the functions on the outputs of the ECA network we can explore different forms of shared, higher order structure in multiple data streams.
FINDING UNDERLYING FACTORS IN TIMESERIES

We compare four neural methods of pre-processing time series data - Principal Component Analysis (PCA) (Karhunen and Joutsensalo 1994), a neural implementation of Factor Analysis (FA), Independent Component Analysis (ICA) (Hyva¨rinen and Oja 1997) and Complexity Pursuit (CP) (Hyva¨rinen 2001) - with a view to subsequently using a multi-layer perceptron (MLP) to forecast on the data set. Our rationale is that forecasting the underlying factors will be easier than forecasting the original time series which is a combination of these factors. The projections of the data onto the filters found by the pre-processing method were fed into the MLP and it was trained to find Least Mean Square Error (LMSE). We show that forecasting the projections on the underlying factors reduces the need to consider the possibility of overtraining the MLP. The last method, CP, achieves by far the best (in terms of least mean square error) performance. Factor Analysis (FA) and particularly Independent Component Analysis (ICA) have the worst performance. Minor modifications to the Complexity Pursuit (CP) method are shown to improve performance in terms of LMSE.
Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit

This paper presents an extension to the learning rules of the Principal Component Analysis Network which has been derived to be optimal for a specific probability density function.W e note this pdf is one of a family of pdfs and investigate the learning rules formed in order to be optimal for several members of this family.We show that the whole family of these learning rules can be viewed as methods for performing Exploratory Projection Pursuit.W e show that these methods provide a simple robust method for the identification of structure in remote sensing images.
Identification of Visual Features Using a Neural Version of Exploratory Projection Pursuit

We develop artificial neural networks which extract structure from visual data. We explore an extension of Hebbian Learning which has been called ? - Insensitive Hebbian Learning and show that it may be thought of as a special case of Maximum Likelihood Hebbian learning and investigate the resulting network with both real and artificial data. We show that the resulting network is able to identify a single orientation of bars from a mixture of horizontal and vertical bars and also it is able to identify local filters from video images.
A New Neural Implementation of Exploratory Projection Pursuit

We investigate an extension of the learning rules in a Principal Component Analysis network which has been derived to be optimal for a specific probability density function(pdf). We note that this probability density function is one of a family of pdfs and investigate the learning rules formed in order to be optimal for several members of this family. We show that, whereas previous authors  have viewed the single member of the family as an extension of PCA, it is more appropriate to view the whole family of learning rules as methods of performing Exploratory Projection Pursuit(EPP). We explore the performance of our method first in response to an artificial data type, then to a real data set.
Exploratory Correlation Analysis

We present a novel unsupervised artificial neural network for the extraction of common features in multiple data sources. This algorithm, which we name Exploratory Correlation Analysis (ECA), is a multi-stream extension of a neural implementation of Exploratory Projection Pursuit (EPP) and has a close relationship with Canonical Correlation Analysis (CCA). Whereas EPP identifies ”interesting” statistical directions in a single stream of data, ECA develops a joint coding of the common underlying statistical features across a number of data streams. It has been shown that the principle of contextual guidance may be used to find a sparse coding of the features in dual natural image patches that is very different from single stream sparse coding experiments. The network only identifies those features which exist in both data streams and thus tend to be fewer in number and more complex in nature.
Classification and ICA using maximum likelihood Hebbian learning

We investigate an extension of Hebbian learning in a principal component analysis network which has been derived to be optimal for a specific probability density function(PDF). We note that this probability density function is one of a family of PDFs and investigate the learning rules formed in order to be optimal for several members of this family. We show that, whereas previous authors have viewed the single member of the family as an extension of PCA, it is more appropriate to view the whole family of learning rules as methods of performing exploratory projection pursuit (EPP). We explore the performance of our method first in response to an artificial data type, then to a real data set.
A robust canonical correlation neural network

We review a neural implementation of canonical correlation analysis and show, using ideas suggested by ridge regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity. We develop a second model which not only performs as well on multicollinear data but also on general data sets. This model allows us to vary a single parameter so that the network is capable of performing partial least squares regression (at one extreme) to canonical correlation analysis (at the other) and every intermediate operation between the two. On multicollinear data, the parameter setting is shown to be important but on more general data no particular parameter setting is required. Finally, the algorithm acts on such data as a smoother in that the resulting weight vectors are much smoother and more interpretable than the weights without the robustification term.
Maximum likelihood Hebbian rules

In this paper, we review an extension of the learning rules in a Principal Component Analysis network which has been derived to be optimal for a specific probability density function. We note that this probability density function is one of a family of pdfs and investigate the learning rules formed in order to be optimal for several members of this family. We show that, whereas previous authors (5) have viewed the single member of the family as an extension of PCA, it is more appropriate to view the whole family of learning rules as methods of performing Exploratory Projection Pursuit. We illustrate this on artificial data sets.
Rectified Gaussian distributions and the formation of local filters from video data

We investigate the use of an unsupervised artificial neural network to form a sparse representation of the underlying causes in a data set. By using fixed lateral connections that are derived from the Rectified Generalised Gaussian distribution, we form a network that is capable of identifying multiple cause structure in visual data and grouping similar causes together on the output response of the network. We show that the network may be used to form local spatiotemporal filters in response to real images contained in video data.