43,216 research outputs found
Intrusion Detection Systems Using Adaptive Regression Splines
Past few years have witnessed a growing recognition of intelligent techniques
for the construction of efficient and reliable intrusion detection systems. Due
to increasing incidents of cyber attacks, building effective intrusion
detection systems (IDS) are essential for protecting information systems
security, and yet it remains an elusive goal and a great challenge. In this
paper, we report a performance analysis between Multivariate Adaptive
Regression Splines (MARS), neural networks and support vector machines. The
MARS procedure builds flexible regression models by fitting separate splines to
distinct intervals of the predictor variables. A brief comparison of different
neural network learning algorithms is also given
Object localisation via action recognition
The aim of this paper is to track objects during their use by humans. The task is difficult because these objects are small, fast-moving and often occluded by the user. We present a novel solution based on cascade action recognition, a learned mapping between body-and object-poses, and a hierarchical extension of importance sampling. During tracking, body pose estimates from a Kinect sensor are classified between action classes by a Support Vector Machine and converted to discriminative object pose hypotheses using a {body, object} pose mapping. They are then mixed with generative hypotheses by the importance sampler and evaluated against the image. The approach out-performs a state of the art adaptive tracker for localisation of 14/15 test implements and additionally gives object classifications and 3D object pose estimates
Comparing Fixed and Adaptive Computation Time for Recurrent Neural Networks
Adaptive Computation Time for Recurrent Neural Networks (ACT) is one of the
most promising architectures for variable computation. ACT adapts to the input
sequence by being able to look at each sample more than once, and learn how
many times it should do it. In this paper, we compare ACT to Repeat-RNN, a
novel architecture based on repeating each sample a fixed number of times. We
found surprising results, where Repeat-RNN performs as good as ACT in the
selected tasks. Source code in TensorFlow and PyTorch is publicly available at
https://imatge-upc.github.io/danifojo-2018-repeatrnn/Comment: Accepted as workshop paper at ICLR 201
Argumentation for machine learning: a survey
Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
Due to the huge availability of documents in digital form, and the deception
possibility raise bound to the essence of digital documents and the way they
are spread, the authorship attribution problem has constantly increased its
relevance. Nowadays, authorship attribution,for both information retrieval and
analysis, has gained great importance in the context of security, trust and
copyright preservation. This work proposes an innovative multi-agent driven
machine learning technique that has been developed for authorship attribution.
By means of a preprocessing for word-grouping and time-period related analysis
of the common lexicon, we determine a bias reference level for the recurrence
frequency of the words within analysed texts, and then train a Radial Basis
Neural Networks (RBPNN)-based classifier to identify the correct author. The
main advantage of the proposed approach lies in the generality of the semantic
analysis, which can be applied to different contexts and lexical domains,
without requiring any modification. Moreover, the proposed system is able to
incorporate an external input, meant to tune the classifier, and then
self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli
Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201
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