137,807 research outputs found
The Santa Fe Artificial Stock Market Re-Examined - Suggested Corrections
This paper rectifies a design problem in the Santa Fe Artificial Stock Market Model. Due to a faulty mutation operator, the resulting bit distribution in the classifier system was systematically upwardly biased, thus suggesting increased levels of technical trading for smaller GA-invocation intervals. The corrected version partly supports the Marimon-Sargent-Hypothesis that adaptive classifier agents in an artificial stock market will always discover the homogeneous rational expectation equilibrium. While agents always find the correct solution of non-bit usage, analyzing the time series data still suggests the existence of two different regimes depending on learning speed. Finally, classifier systems and neural networks as data mining techniques in artificial stock markets are discussed.Asset Pricing; Learning; Financial Time Series; Genetic Algorithms; Classifier Systems; Agent-Based Simulation
A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers
Ā© 2015, Springer Science+Business Media New York. Learning classifier systems (LCS) are population-based reinforcement learners that were originally designed to model various cognitive phenomena. This paper presents an explicitly cognitive LCS by using spiking neural networks as classifiers, providing each classifier with a measure of temporal dynamism. We employ a constructivist model of growth of both neurons and synaptic connections, which permits a genetic algorithm to automatically evolve sufficiently-complex neural structures. The spiking classifiers are coupled with a temporally-sensitive reinforcement learning algorithm, which allows the system to perform temporal state decomposition by appropriately rewarding āmacro-actionsā, created by chaining together multiple atomic actions. The combination of temporal reinforcement learning and neural information processing is shown to outperform benchmark neural classifier systems, and successfully solve a robotic navigation task
Recommended from our members
An incremental approach to MSE-based feature selection
Feature selection plays an important role in classification systems. Using classifier error rate as the evaluation function, feature selection is integrated with incremental training. A neural network classifier is implemented with an incremental training approach to detect and discard irrelevant features. By learning attributes one after another, our classifier can find directly the attributes that make no contribution to classification. These attributes are marked and considered for removal. Incorporated with a Minimum Squared Error (MSE) based feature ranking scheme, four batch removal methods based on classifier error rate have been developed to discard irrelevant features. These feature selection methods reduce the computational complexity involved in searching among a large number of possible solutions significantly. Experimental results show that our feature selection methods work well on several benchmark problems compared with other feature selection methods. The selected subsets are further validated by a Constructive Backpropagation (CBP) classifier, which confirms increased classification accuracy and reduced training cost
End-to-end Phoneme Sequence Recognition using Convolutional Neural Networks
Most phoneme recognition state-of-the-art systems rely on a classical neural
network classifiers, fed with highly tuned features, such as MFCC or PLP
features. Recent advances in ``deep learning'' approaches questioned such
systems, but while some attempts were made with simpler features such as
spectrograms, state-of-the-art systems still rely on MFCCs. This might be
viewed as a kind of failure from deep learning approaches, which are often
claimed to have the ability to train with raw signals, alleviating the need of
hand-crafted features. In this paper, we investigate a convolutional neural
network approach for raw speech signals. While convolutional architectures got
tremendous success in computer vision or text processing, they seem to have
been let down in the past recent years in the speech processing field. We show
that it is possible to learn an end-to-end phoneme sequence classifier system
directly from raw signal, with similar performance on the TIMIT and WSJ
datasets than existing systems based on MFCC, questioning the need of complex
hand-crafted features on large datasets.Comment: NIPS Deep Learning Workshop, 201
On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
Deep learning based medical image classifiers have shown remarkable prowess
in various application areas like ophthalmology, dermatology, pathology, and
radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD)
systems in real clinical setups is severely limited primarily because their
decision-making process remains largely obscure. This work aims at elucidating
a deep learning based medical image classifier by verifying that the model
learns and utilizes similar disease-related concepts as described and employed
by dermatologists. We used a well-trained and high performing neural network
developed by REasoning for COmplex Data (RECOD) Lab for classification of three
skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and
performed a detailed analysis on its latent space. Two well established and
publicly available skin disease datasets, PH2 and derm7pt, are used for
experimentation. Human understandable concepts are mapped to RECOD image
classification model with the help of Concept Activation Vectors (CAVs),
introducing a novel training and significance testing paradigm for CAVs. Our
results on an independent evaluation set clearly shows that the classifier
learns and encodes human understandable concepts in its latent representation.
Additionally, TCAV scores (Testing with CAVs) suggest that the neural network
indeed makes use of disease-related concepts in the correct way when making
predictions. We anticipate that this work can not only increase confidence of
medical practitioners on CAD but also serve as a stepping stone for further
development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural
Networks (IJCNN) 202
Reasoning about ideal interruptible moments: A soft computing implementation of an interruption classifier in free-form task environments
Current trends in society and technology make the concept of interruption a central human computer interaction problem. In this work, a novel soft computing implementation for an Interruption Classifier was designed, developed and evaluated that draws from a user model and real-time observations of the user\u27s actions as s/he works on computer-based tasks to determine ideal times to interact with the user. This research is timely as the number of interruptions people experience daily has grown considerably over the last decade. Thus, systems are needed to manage interruptions by reasoning about ideal timings of interactions. This research shows: (1) the classifier incorporates a user model in itsā reasoning process. Most of the research in this area has focused on task-based contextual information when designing systems that reason about interruptions; (2) the classifier performed at 96% accuracy in experimental test scenarios and significantly out-performed other comparable systems; (3) the classifier is implemented using an advanced machine learning technologyāan Adaptive Neural-Fuzzy Inference Systemāthis is unique since all other systems use Bayesian Networks or other machine learning tools; (4) the classifier does not require any direct user involvementāin other systems, users must provide interruption annotations while reviewing video sessions so the system can learn; and (5) a promising direction for reasoning about interruptions for free-form tasksāthis is largely an unsolved problem
- ā¦