19 research outputs found
Adaptive Online Sequential ELM for Concept Drift Tackling
A machine learning method needs to adapt to over time changes in the
environment. Such changes are known as concept drift. In this paper, we propose
concept drift tackling method as an enhancement of Online Sequential Extreme
Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by
adding adaptive capability for classification and regression problem. The
scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme
that works well to handle real drift, virtual drift, and hybrid drift. The
AOS-ELM also works well for sudden drift and recurrent context change type. The
scheme is a simple unified method implemented in simple lines of code. We
evaluated AOS-ELM on regression and classification problem by using concept
drift public data set (SEA and STAGGER) and other public data sets such as
MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value
compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice
does not need hidden nodes increase, we address some issues related to the
increasing of the hidden nodes such as error condition and rank values. We
propose taking the rank of the pseudoinverse matrix as an indicator parameter
to detect underfitting condition.Comment: Hindawi Publishing. Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 8091267, 17 pages Received 29 January 2016,
Accepted 17 May 2016. Special Issue on "Advances in Neural Networks and
Hybrid-Metaheuristics: Theory, Algorithms, and Novel Engineering
Applications". Academic Editor: Stefan Hauf
Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases
For big data analysis, high computational cost for Bayesian methods often
limits their applications in practice. In recent years, there have been many
attempts to improve computational efficiency of Bayesian inference. Here we
propose an efficient and scalable computational technique for a
state-of-the-art Markov Chain Monte Carlo (MCMC) methods, namely, Hamiltonian
Monte Carlo (HMC). The key idea is to explore and exploit the structure and
regularity in parameter space for the underlying probabilistic model to
construct an effective approximation of its geometric properties. To this end,
we build a surrogate function to approximate the target distribution using
properly chosen random bases and an efficient optimization process. The
resulting method provides a flexible, scalable, and efficient sampling
algorithm, which converges to the correct target distribution. We show that by
choosing the basis functions and optimization process differently, our method
can be related to other approaches for the construction of surrogate functions
such as generalized additive models or Gaussian process models. Experiments
based on simulated and real data show that our approach leads to substantially
more efficient sampling algorithms compared to existing state-of-the art
methods
A survey of handwritten character recognition with MNIST and EMNIST
This article belongs to the Special Issue Computer Vision and Pattern Recognition in the Era of Deep Learning.This paper summarizes the top state-of-the-art contributions reported on the MNIST dataset for handwritten digit recognition. This dataset has been extensively used to validate novel techniques in computer vision, and in recent years, many authors have explored the performance of convolutional neural networks (CNNs) and other deep learning techniques over this dataset. To the best of our knowledge, this paper is the first exhaustive and updated review of this dataset; there are some online rankings, but they are outdated, and most published papers survey only closely related works, omitting most of the literature. This paper makes a distinction between those works using some kind of data augmentation and works using the original dataset out-of-the-box. Also, works using CNNs are reported separately; as they are becoming the state-of-the-art approach for solving this problem. Nowadays, a significant amount of works have attained a test error rate smaller than 1% on this dataset; which is becoming non-challenging. By mid-2017, a new dataset was introduced: EMNIST, which involves both digits and letters, with a larger amount of data acquired from a database different than MNIST's. In this paper, EMNIST is explained and some results are surveyed
ADIC: Anomaly Detection Integrated Circuit in 65nm CMOS utilizing Approximate Computing
In this paper, we present a low-power anomaly detection integrated circuit
(ADIC) based on a one-class classifier (OCC) neural network. The ADIC achieves
low-power operation through a combination of (a) careful choice of algorithm
for online learning and (b) approximate computing techniques to lower average
energy. In particular, online pseudoinverse update method (OPIUM) is used to
train a randomized neural network for quick and resource efficient learning. An
additional 42% energy saving can be achieved when a lighter version of OPIUM
method is used for training with the same number of data samples lead to no
significant compromise on the quality of inference. Instead of a single
classifier with large number of neurons, an ensemble of K base learner approach
is chosen to reduce learning memory by a factor of K. This also enables
approximate computing by dynamically varying the neural network size based on
anomaly detection. Fabricated in 65nm CMOS, the ADIC has K = 7 Base Learners
(BL) with 32 neurons in each BL and dissipates 11.87pJ/OP and 3.35pJ/OP during
learning and inference respectively at Vdd = 0.75V when all 7 BLs are enabled.
Further, evaluated on the NASA bearing dataset, approximately 80% of the chip
can be shut down for 99% of the lifetime leading to an energy efficiency of
0.48pJ/OP, an 18.5 times reduction over full-precision computing running at Vdd
= 1.2V throughout the lifetime.Comment: 1
An Event based Prediction Suffix Tree
This article introduces the Event based Prediction Suffix Tree (EPST), a
biologically inspired, event-based prediction algorithm. The EPST learns a
model online based on the statistics of an event based input and can make
predictions over multiple overlapping patterns. The EPST uses a representation
specific to event based data, defined as a portion of the power set of event
subsequences within a short context window. It is explainable, and possesses
many promising properties such as fault tolerance, resistance to event noise,
as well as the capability for one-shot learning. The computational features of
the EPST are examined in a synthetic data prediction task with additive event
noise, event jitter, and dropout. The resulting algorithm outputs predicted
projections for the near term future of the signal, which may be applied to
tasks such as event based anomaly detection or pattern recognition