A Note on Support Vector Machines Degeneracy

When training Support Vector Machines (SVMs) over non-separable data sets, one sets the threshold $b$ using any dual cost coefficient that is strictly between the bounds of $0$ and $C$. We show that there exist SVM training problems with dual optimal solutions with all coefficients at bounds, but that all such problems are degenerate in the sense that the "optimal separating hyperplane" is given by ${f w} = {f 0}$, and the resulting (degenerate) SVM will classify all future points identically (to the class that supplies more training data). We also derive necessary and sufficient conditions on the input data for this to occur. Finally, we show that an SVM training problem can always be made degenerate by the addition of a single data point belonging to a certain unboundedspolyhedron, which we characterize in terms of its extreme points and rays.

Evolving features-algorithms knowledge map to support NIDS data intelligence and learning loop architecting – a generalised approach to NIDS pattern feature space knowledge processing

This paper describes a proposed new approach to the Computer Network Security Intrusion Detection Systems (NIDS) application domain knowledge processing focused on a topic map technology-enabled representation of features of the threat pattern space as well as the knowledge of situated efficacy of alternative candidate algorithms for pattern recognition within the NIDS domain. Thus an integrative knowledge representation framework for virtualisation, data intelligence and learning loop architecting in the NIDS domain is described together with specific aspects of its deployment.

Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

Using problem-based learning in a chemistry practical class for pharmacy students and engaging them with feedback

Objective: To introduce a new approach to problem based learning (PBL) used in the context of medicinal chemistry practical class teaching pharmacy students. Design: The described chemistry practical is based on independent studies by small groups of undergraduate students (4-5), who design their own practical work taking relevant professional standards into account. Students are carefully guided by feedback and acquire a set of skills important to their future profession as healthcare professionals. This model has been tailored to the application of PBL in a chemistry practical class setting for a large student cohort (150 students). Assessment: The achievement of learning outcomes is based on the submission of relevant documentation including a certificate of analysis, in addition to peer assessment. Some of the learning outcomes are also assessed in the final written examination at the end of the academic year. Conclusion: The described design of a novel PBL chemistry laboratory course for pharmacy students has been found to be successful. Self-reflective learning and engagement with feedback were encouraged, and students enjoyed the challenging learning experience. Skills that are highly essential for the students’ future careers as healthcare professionals are promoted.

Students’ Role in Distance Learning

1. IntroductionMuch of the support that students have in a traditional classroom is absent in a distance learning course. In the traditional classroom, the learner is together with his or her classmates and the teacher; learning is socially embedded. Students can talk to each other and may learn from each other as they go through the learning process together. They also witness the teacher’s expression of the knowledge firsthand. The class participants communicate to each other not only through their words, but also through their gestures, facial expressions and tone of voice, and the teacher can observe the students’ progress and provide guidance and feedback in an as-needed basis. Further, through the habit of meeting in a regular place at a regular time, the participants reinforce their own and each other’s commitment to the course. A distance course must somehow provide learners other kinds of supports so that the distance learner also has a sense of connection with a learning community; can benefit from interaction with peers who are going through a similar learning process; receives feedback that allows him or her to know how he or she is progressing; and is guided enough so that he or she continues to progress towards the learning objectives. This cannot be accomplished if the distance course does not simultaneously promote student autonomy, for the distance course format requires students to take greater responsibility for their own learning. This chapter presents one distance learning course that was able to address all of these goals. The English Department at Högskolan Dalarna, Sweden, participates in a distance learning program with Vietnam National University. Students enrolled in this program study half-time for two years to complete a Master’s degree in English Linguistics. The distance courses in this program all contain two types of regular class meetings: one type is student-only seminars conducted through text chat, during which students discuss and complete assignments that prepare them for the other type of class meeting, also conducted through text chat, where the teacher is present and is the one to lead the discussion of seminar issues and assignments. The inclusion of student-only seminars in the course design allows for student independence while at the same time it encourages co-operation and solidarity. The teacher-led seminars offer the advantages of a class led by an expert.In this chapter, we present chatlog data from Vietnamese students in one distance course in English linguistics, comparing the role of the student in both student-only and teacher-led seminars. We discuss how students navigate their participation roles, through computer-mediated communication (CMC), according to seminar type, and we consider the emerging role of the autonomous student in the foreign-language medium, distance learning environment. We close by considering aspects of effective design of distance learning courses from the perspective of a foreign language (FL) environment.

Institutional Support for Experiential Learning

This brief details institutional support structures and program structures for experiential learning at small colleges. It also examines credit structures associated with experiential learning, experiential learning as a graduation requirement, and program assessments.

HOW FAR do WE GET USING MACHINE LEARNING BLACK-BOXES?

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evaluation of Bone Insertion Level of Support Teeth in Class I Mandibular Removable Partial Denture Associated With an Osseointegrated Implant: A Study Using Finite Element Analysis

Purpose: This study evaluated the influence of distal extension removable partial denture associated with implant in cases of different bone level of abutment tooth, using 2D finite element analysis.Materials and Methods: Eight hemiarch models were simulated: model A-presenting tooth 33 and distal extension removable partial denture replacing others teeth, using distal rest connection and no bone lost; model B-similar to model A but presenting distal guide plate connection; model C-similar to model A but presenting osseointegrated implant with ERA retention system associated under prosthetic base; model D-similar to model B but presenting osseointegrated implant as described in model C; models E, F, G, and H were similar to models A, B, C, and D but presenting reduced periodontal support around tooth 33. Using ANSYS 9.0 software, the models were loaded vertically with 50 N on each cusp tip. For results, von Mises Stress Maps were plotted.Results: Maximum stress value was encountered in model G (201.023 MPa). Stress distribution was concentrated on implant and retention system. The implant/removable partial denture association decreases stress levels on alveolar mucosa for all models.Conclusions: Use of implant and ERA system decreased stress concentrations on supporting structures in all models. Use of distal guide plate decreased stress levels on abutment tooth and cortical and trabecular bone. Tooth apex of models with reduced periodontal support presented increased stress when using distal rest. (Implant Dent 2011;20:192-201)

Information Visualization to support decision making in the context of distance learning: A study with VLE TelEduc

Digital data sets constitute rich sources of information, which can be extracted and evaluated applying computational tools, for example, those ones for Information Visualization. Web-based applications, such as social network environments, forums and virtual environments for Distance Learning, are good examples for such sources. The great amount of data has direct impact on processing and analysis tasks. This paper presents the computational tool Mapper, defined and implemented to use visual representations - maps, graphics and diagrams - for supporting the decision making process by analyzing data stored in Virtual Learning Environment TelEduc-Unesp. © 2012 IEEE.

Learning kernels for support vector machines with polynomial powers of sigmoid

In the pattern recognition research field, Support Vector Machines (SVM) have been an effectiveness tool for classification purposes, being successively employed in many applications. The SVM input data is transformed into a high dimensional space using some kernel functions where linear separation is more likely. However, there are some computational drawbacks associated to SVM. One of them is the computational burden required to find out the more adequate parameters for the kernel mapping considering each non-linearly separable input data space, which reflects the performance of SVM. This paper introduces the Polynomial Powers of Sigmoid for SVM kernel mapping, and it shows their advantages over well-known kernel functions using real and synthetic datasets.