Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models

This multidisciplinary study focuses on the application and comparison of several topology preserving mapping models upgraded with some classifier ensemble and boosting techniques in order to improve those visualization capabilities. The aim is to test their suitability for classification purposes in the field of food industry and more in particular in the case of dry cured ham. The data is obtained from an electronic device able to emulate a sensory olfative taste of ham samples. Then the data is classified using the previously mentioned techniques in order to detect which batches have an anomalous smelt (acidity, rancidity and different type of taints) in an automated way

On the application of connectionist models for pattern recognition, robotics and computer vision: A technical report

Connectionist modeis, commonly referred to as neural networks, are computing models in which large numbers of processing units are connected to each other with variable "weight". These weight values represent the "strength" of the connection between two units, which can be positive (excitatory, i.e. exciting the activity of a unit) or negative (inhibitory, i.e. suppressing the activity of a unit). The functional behavior of a connectionist network is determined by these weight values. Changing the weight values or the topology of the network results in different nets with different applications. It has been demonstrated th at connectionist models are well suited to implement some pattem recognition, optimization and/or adaptive leaming techniques, in a massively parallel, fault resistant manner. The aim of this report is to provide an overview of the literature in this field, and to investigate the practical applications of connectionist models for pattern recognition, robotics and computer vision. From the perspective of an engineer, the tools provided by connectionist models are compared to other available tools and it is shown in which cases these tools are more efficient than other implementations. The improved efficiency can be based on (one of) the following properties: the massive parallelism, the robustness of the implementation, the large variety of algorithms that can be implemented in a connectionist network, the availability of a suitable technology for hardware implementations, and finally on the specific properties of some particular models.Delft University of Technolog

Small Sample Behavior of Multi-Layer Feedforward Network Classifiers: Theoretical and Practical Aspects

Applied Science

A nonlinear projection method based on Kohonen's topology preserving maps

Abstruct-A nonlinear projection method is presented to vi-sualize higb-dimensional data as a two-dimensional image. The proposed method b based on the topotogV p “ mpp-ping algorithm d Kohonen [13H16]. The tapology preserving mapping algorithm is used to trpin a two-dimensional network structure. Then the interpoint dbtances in tbe feature space between the units in the network are graphidly cusplayea to show the underlying StruCtuFe of the data. Fartheimore, we will present and discuss a new method to qnadfy how well a topologv preserving mapping algorithm maps the bigbdbensiod input data onto the network stmeture, This will be used to compare our projection method with a well-k~~own method of Sa”on [SI. Experiments indicate that the performance of the Koho-nen projection method is con~pambk or better than Sammon’s method for the purpose of clparsurine dasEcnd data. Another advantage of the metbod is that its tbe-complesity only depends on the resolution of the outpot irmrse, and not on the size of the dataset. A disadvantage, however, is the large amount of CPU time required. I

Robust Curve Detection using a Radon Transform in Orientation Space Applied to Fracture Detection in Borehole Images

Applied Science