Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time of training the model. In this paper we provide stateof -the-art review in the area of novelty detection based on statistical approaches. The second part paper details novelty detection using neural networks. As discussed, there are a multitude of applications where novelty detection is extremely important including signal processing, computer vision, pattern recognition, data mining, and robotics.

Abstract—In this paper, we present a new framework for novelty detection. The framework evaluates neural networks as adaptive classifiers that are capable of novelty detection and retraining on the basis of newly discovered information. We apply our newly developed model to the application area of object recognition in video. This paper details the tools and methods needed for novelty detection such that data from unknown classes can be reliably rejected without any a priori knowledge of its characteristics. The rejected data is postprocessed to determine which samples can be manually labeled of a new type and used for retraining. In this paper, we compare the proposed framework with other novelty detection methods and discuss the results of adaptive retraining of neural network to recognize further unseen data containing the newly added objects. Index Terms—Scene analysis, neural networks, adaptive classifiers, novelty detection. æ

Object-oriented visualization-based software systems for biomedical data analysis must deal with complex and voluminous datasets within a flexible yet intuitive graphical user interface. In a research environment, the development of such systems are difficult to manage due to rapidly changing requirements, incorporation of newly developed algorithms, and the needs imposed by a diverse user base. One issue that research supervisors must contend with is an assessment of the quality of the system's software objects with respect to their extensibility, reusability, clarity, and efficiency.

Effectiveness and clarity of software objects, their adherence to coding standards and programming habits of programmers are important features of overall quality of software systems. This paper proposes an approach towards a quantitative software quality assessment with respect to extensibility, reusability, clarity and efficiency. It exploits techniques of Computational Intelligence (CI) that are treated as a consortium of granular computing, neural networks and evolutionary techniques. In particular, we take advantage of self-organizing maps to gain a better insight into the data, and study genetic decision trees -- a novel algorithmic framework to carry out classification of software objects with respect to their quality. Genetic classifiers serve as a "quality filter" for software objects. Using these classifiers, a system manager can predict quality of software objects and identify low quality objects for review and possible revision. The approach is applied to an object-oriented visualization-based software system for biomedical data analysis.

Machine learning techniques are widely used in the analysis of biomedical datasets. Modern devices tend to produce voluminous, high-dimensional datasets for which medical practitioners require high-performance, userfriendly programs and researchers need effective algorithm development and testing platforms. Interactive development systems, such as MATLAB, provide for rapid prototyping of algorithms and visualization but at the cost of computational efficiency. We present Scopira, a C++, open source programming framework for the development of biomedical data analysis applications. 1.

In this paper we present a new framework for novelty detection. The framework evaluates neural networks as adaptive classifiers that are capable of novelty detection and retraining on the basis of newly discovered information. We apply our newly developed model to the application area of object recognition in video. The application is however not limited to scene analysis and the basic methodology can be easily extended to other areas. This paper details the tools and methods needed for novelty detection such that data from unknown classes can be reliably rejected without any a priori knowledge of its characteristics. The rejected data is post-processed to determine which samples can be manually labelled of a new type and used for retraining. In this paper we compare the proposed framework with a nave solution and discuss the results of retraining neural network to recognise further unseen data containing the newly added objects.

Functional magnetic resonance imaging (fMRI) is a complex imaging modality that provides high resolution, non-invasive maps of neural activity in brain tissue. Neuroscientists use fMRI to probe brain function using complex cognitive and linguistic experiments. An important aspect of these experiments is the visualization of neural activations over a period of time as manifested by voxel intensity of two ( or three ) dimensional images across the temporal analysis dimension.

Noveltydetection is the identi cation of new or unknown data or signal that a machine learning system is not aware of during training. Noveltydetection is one of the fundamental requirements of a good classi cation or identi cation system since sometimes the test data contains information about objects that were not known at the time of training the model. In this paper we provide state-of-the-art review in the area of noveltydetection based on statistical approaches. The second part paper details noveltydetection using neural networks. As discussed, there are a multitude of applications where novelty detection is extremelyimportant including signal processing, computer vision, pattern recognition, data mining, and robotics.