Semi-supervised learning for relevance feedback on image retrieval tasks

Relevance feedback approaches have been established as an important tool for interactive search, enabling users to express their needs. However, in view of the growth of multimedia collections available, the user efforts required by these methods tend to increase as well, demanding approaches for reducing the need of user interactions. In this context, this paper proposes a semi-supervised learning algorithm for relevance feedback to be used in image retrieval tasks. The proposed semi-supervised algorithm aims at using both supervised and unsupervised approaches simultaneously. While a supervised step is performed using the information collected from the user feedback, an unsupervised step exploits the intrinsic dataset structure, which is represented in terms of ranked lists of images. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors and different datasets. The proposed approach was also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of the proposed approach.

Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut

Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.

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.

A self-learning simulated annealing algorithm for global optimizations of electromagnetic devices

A self-learning simulated annealing algorithm is developed by combining the characteristics of simulated annealing and domain elimination methods. The algorithm is validated by using a standard mathematical function and by optimizing the end region of a practical power transformer. The numerical results show that the CPU time required by the proposed method is about one third of that using conventional simulated annealing algorithm.

How to progress from discourse to practice? a new agenda for change in medical schools into the next decade

In the context of medical school instruction, the segmented approach of a focus on specialties and excessive use of technology seem to hamper the development of the professional-patient relationship and an understanding of the ethics of this relationship. The real world presents complexities that require multiple approaches. Engagement in the community where health competence is developed allows extending the usefulness of what is learned. Health services are spaces where the relationship between theory and practice in health care are real and where the social role of the university can be revealed. Yet some competencies are still lacking and may require an explicit agenda to enact. Ten topics are presented for focus here: environmental awareness, involvement of students in medical school, social networks, interprofessional learning, new technologies for the management of care, virtual reality, working with errors, training in management for results, concept of leadership, and internationalization of schools. Potential barriers to this agenda are an underinvestment in ambulatory care infrastructure and community-based health care facilities, as well as in information technology offered at these facilities; an inflexible departmental culture; and an environment centered on a discipline-based medical curriculum.

Bandit-Based Algorithms for Budgeted Learning

We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ labels but has to pay for each attribute that is specified. This learning model is appropriate in many areas, including medical applications. We present new algorithms for choosing which attributes to purchase of which examples in the budgeted learning model based on algorithms for the multi-armed bandit problem. All of our approaches outperformed the current state of the art. Furthermore, we present a new means for selecting an example to purchase after the attribute is selected, instead of selecting an example uniformly at random, which is typically done. Our new example selection method improved performance of all the algorithms we tested, both ours and those in the literature.

An Extended Kernel for Generalized Multiple-Instance Learning

The multiple-instance learning (MIL) model has been successful in areas such as drug discovery and content-based image-retrieval. Recently, this model was generalized and a corresponding kernel was introduced to learn generalized MIL concepts with a support vector machine. While this kernel enjoyed empirical success, it has limitations in its representation. We extend this kernel by enriching its representation and empirically evaluate our new kernel on data from content-based image retrieval, biological sequence analysis, and drug discovery. We found that our new kernel generalized noticeably better than the old one in content-based image retrieval and biological sequence analysis and was slightly better or even with the old kernel in the other applications, showing that an SVM using this kernel does not overfit despite its richer representation.

NAMA National Conference a Learning Experience for Nebraska Students

Change was in the air at the 2010 National Agri- Marketing Association (NAMA) Annual Conference held April 20-23 in Kansas City, Missouri. Students and professionals alike were given the opportunity to rub shoulders with, and hear nationally known speakers engaging audiences on topics such as the new rules of marketing and publication relations, measuring the effectiveness of social media and strategy, and brand communication.

The Shaping of a New Identity: Continuation of the Transformation Process of People of Color on White University College Campuses Into "White" People of Color

“Our study will show how the pyramidal structure as a permanent feature of every aspect of American society continues to function in the same manner at institutions of higher learning.”

Learning generalization in problem solving by a blue-fronted parrot (Amazona aestiva)

Pepperberg (The Alex studies: cognitive and communicative abilities of gray parrots. Harvard University Press, Cambridge;1999) showed that some of the complex cognitive capabilities found in primates are also present in psittacine birds. Through the replication of an experiment performed with cotton-top tamarins (Saguinus oedipus oedipus) by Hauser et al. (Anim Behav 57:565-582; 1999), we examined a blue-fronted parrot`s (Amazona aestiva) ability to generalize the solution of a particular problem in new but similar cases. Our results show that, at least when it comes to solving this particular problem, our parrot subject exhibited learning generalization capabilities resembling the tamarins`.

Playing to Create New Ways of Playing: A Child with Prader-Willi Syndrome

This investigation evaluates the possibility of constructing new ways of playing for a child with Prader-Willi syndrome, by means of occupational therapy. It is a qualitative study which makes use of the case study methodology, whose starting point is the clinical intervention as data collect field. It also presents a short revision of the literature to subside discussions and reflections. It was observed that through the playing experience the occupational therapist led the child to know his own limitations and possibilities, by making him discover new ways of doing activities. Observing the therapist and learning with her, the patient experienced different situations throughout the therapeutic relationship, what enabled him to experiment them in his everyday life. Finally, this study aims at showing the clinical reasoning of an occupational therapist with a view to demonstrate Brazilian therapeutical conduct.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Applying validated quality indicators to surgical antibiotic prophylaxis in a Brazilian hospital: Learning what should be learned

Background: Compliance with the best surgical antibiotic prophylaxis practice is usually low despite many published guidelines. Objective: This study investigated compliance with the Hospital Infection Control Committee guideline for antibiotic prophylaxis in a Brazilian hospital using quality indicators. Methods: A retrospective study was carried out from November 2009 to March 2010. Medical records from adult inpatients undergoing cardiac, neurologic, and orthopedic clean surgeries were included. The full compliance index was considered 100% when the antibiotic prophylaxis showed adequacy in all evaluated attributes. Analyses were conducted with 5% significance. Results: Medical records from 101 cardiac, 128 neurologic, and 519 orthopedic surgical patients were evaluated. The compliance index was 4.9%, and the compliance index according to specialty was 5.8%, 3.1%, and 3.0%, respectively, for orthopedic, neurologic, and cardiac surgeries. The attribute route of administration produced the best outcomes, whereas the attribute duration of antibiotic prophylaxis produced the worst. No association was identified between compliance to the attributes and patient characteristics. Conclusion: This study showed a low level of adherence to Hospital Infection Control Committee guidelines for antibiotic prophylaxis. This suggests that different strategies should be implemented to promote the best possible practice in the field of antibiotic prophylaxis with greater surgeon engagement. Copyright (C) 2012 by the Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.