New Incremental Learning Algorithm for Semi-Supervised Support Vector Machine

This study investigates the joint power allocation and energy cooperation problem in a multiuser downlink distributed antenna system with hybrid energy supply. The authors focus on the energy efficiency (EE) maximization problem for three different types of precoding, zero‐forcing (ZF), general beamforming method and conjugate‐beamforming. For ZF precoding, they apply fractional programming and reform the optimization problem to a convex one. An iterative algorithm to deal with the fractional objective function is presented. Whereas the problem is non‐convex for general beamforming with user interference, they take maximum ratio transmission as an example and adopt a set of transformation and approximation based on the difference of convex (DC) programming. Furthermore, for conjugate‐beamforming, they first transform the quadratic signal to interference plus noise power ratio into a trackable form and then DC programming is applied. A two‐loop algorithm for the general beamforming and conjugate‐beamforming is presented with fractional programming in the inner loop and DC programming in the outer loop. Simulations show that the proposed algorithm improved EE significantly and indicate that energy cooperation can contribute to a higher EE. It also reveals that ZF achieves better EE with small noise variance while conjugate beamforming in high noise circumstance.

In this paper, we consider a method to solve the device-free localization (DFL) problem that is able to detect spatial obstruction via wireless network. A dictionary learning approach with difference of convex (DC) programming and DC algorithm is proposed to indicate target location based on learning data. By measuring the variation in the received signal strength of the wireless links indicating the locations of the obstructions, the physical target in the monitoring area can be estimated through a learned dictionary. We show that the DFL problem can be efficiently formulated as a non-convex optimization problem. We adopt a penalty function called the minimax concave penalty, which possesses good properties in terms of seeking sparsity, and solve the non-convex optimization problem using DC programming. Furthermore, the localization accuracy achieved during the path-tracking task is further improved by the proposed tracking neighborhood rule. The rule provides a solution for increasing the localization accuracy under time-varying conditions generated by sampling channels of sensor networks under noisy conditions. The proposed approach is validated on a real-world dataset and has the potential to be adopted flexibly in DFL applications.

Incremental Learning from Positive Data

This paper proves the problem of losing incremental samples' information of the present SVM incremental learning algorithm from both theoretic and experimental aspects, and proposes a new incremental learning algorithm with support vector machine based on hyperplane-distance. According to the geometric character of support vector, the algorithm uses Hyperplane-Distance to extract the samples, selects samples which are most likely to become support vector to form the vector set of edge, and conducts the support vector machine training on the vector set. This method reduces the number of training samples and effectively improves training speed of incremental learning. The results of experiment performed on Chinese webpage classification show that this algorithm can reduce the number of training samples effectively and accumulate historical information. The HD-SVM algorithm has higher training speed and better precision of classification.

Efficient incremental planning and learning with multi-valued decision diagrams

This paper proposed a diagnostic supporting tool - i <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> DiaKAW (intelligent and interactive knowledge acquisition workbench), which automatically extracts useful knowledge from massive medical data by applying various data mining techniques for supporting real medical diagnosis. To fulfill this effort, i <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> DiaKAW has been developed to provide a novel dynamic on-line decision tree learning scheme - i <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> Learning (intelligent and incremental learning) methodology that is able to incrementally incorporate the new incoming data with an existing decision tree without relearning the entire tree from scratch. i <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> Learning strategy makes up for the traditional incremental decision tree learning algorithms by concerning the new available features in addition to the new coming instances. Such theory is a new attempt and is designed specifically for bridging the current gaps. The empirical results demonstrate a comparison of performance of various classification algorithms on several real-life datasets from UCI repository.

Incremental learning for [formula omitted]-Support Vector Regression

An incremental one-class learning algorithm is proposed for the purpose of outlier detection. Outliers are identified by estimating - and thresholding - the probability distribution of the training data. In the early stages of training a non-parametric estimate of the training data distribution is obtained using kernel density estimation. Once the number of training examples reaches the maximum computationally feasible limit for kernel density estimation, we treat the kernel density estimate as a maximally-complex Gaussian mixture model, and keep the model complexity constant by merging a pair of components for each new kernel added. This method is shown to outperform a current state-of-the-art incremental one-class learning algorithm (Incremental SVDD [5]) on a variety of datasets, while requiring only an upper limit on model complexity to be specified.KeywordsTraining DataGaussian Mixture ModelOutlier DetectionKernel Density EstimationNormal ClassThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A-iLearn: An adaptive incremental learning model for spoof fingerprint detection

Detecting spamming activities in a campus network using incremental learning

Bayesian network utilizes graphical model to describe dependencies among variables in probabilistic way, it is one of the most important model for uncertainty processing in Artificial Intelligence. Incremental learning of Bayesian networks has been received more attentions in recent years, in this paper a novel method is proposed to learn Bayesian network from incremental data. In this method, a novel incremental scoring function is designed to adaptively adjust the tendency of matching new and old data in the process of incremental learning. We propose an improved adaptive incremental structure learning algorithm for Bayesian network. Theoretical analysis and experimental results both demonstrate the proposed method outperforms other state-of-the-art methods.

Since it is difficult to establish precise physical model of complex systems, time series prediction is often used to predict their health trend and running state. Aiming at online prediction, we proposed a new scheme to fix the problems of time series online prediction, which is based on LS-SVR model and incremental learning algorithm. The scheme includes two aspects. Firstly, by replacing single kernel with new fixed kernel consisting of several basis kernels, a better information mapping in high dimension is obtained; secondly, by establishing new LS-SVR model without bias term b, the calculation process with incremental learning is simplified. Prediction experiment is performed via certain avionics application. The results indicate preliminarily that the proposed scheme is an effective prediction approach for its good prediction precision and less computing time. The method will be useful in actual application.

This paper introduces several nonlinear multi-model ensemble techniques for multiple chaotic models in high-dimensional phase space by means of artificial neural networks. A chaotic model is built by way of the time-delayed phase space reconstruction of the time series from observables. Several predictive global and local models, including Multi-layered Perceptron Neural Network (MLP-NN), are constructed and a number of multi-model ensemble techniques are implemented to produce more accurate hybrid models. One of these techniques is the nonlinear multi-model ensemble using one kind of dynamic neural network so-called Focused Time Delay Neural Network (FTDNN) with batch and incremental learning algorithms. The proposed techniques were used and tested for predicting storm surge dynamics in the North Sea. The results showed that the accuracy of multi-model ensemble predictions is generally improved in comparison to the one by single models. An FTDNN with incremental learning is more desirable for real-time operation, however in our experiments it was less accurate than batch learning.

New incremental learning algorithm using extended data expression, based on probabilistic compounding, is presented in this paper. Incremental learning algorithm generates an ensemble of weak classifiers and compounds these classifiers to a strong classifier, using a weighted majority voting, to improve classification performance. We introduce new probabilistic weighted majority voting founded on extended data expression. In this case class distribution of the output is used to compound classifiers. UChoo, a decision tree classifier for extended data expression, is used as a base classifier, as it allows obtaining extended output expression that defines class distribution of the output. Extended data expression and UChoo classifier are powerful techniques in classification and rule refinement problem. In this paper extended data expression is applied to obtain probabilistic results with probabilistic majority voting. To show performance advantages, new algorithm is compared with Learn++, an incremental ensemble-based algorithm.

Most facial expression recognition (FER) systems used facial expression data created during a short period of time and this data is used for learning/training of FER systems. There are many facial expression patterns (i.e. a particular expression can be represented in many different patterns) which cannot be generated and used as learning/training data in a short time. Therefore, in order to maintain its high accuracy and robustness for a long time of a facial expression recognition system, the classifier should be evolved adaptively over time and space. We proposed a facial expression recognition system that has the aptitude of incrementally learning and thus can learn all possible patterns of expressions that may be generated in feature. After extraction of region of interest (face), the system extracts Speeded-Up Robust Features (SURF). A novel SURF descriptor template based nearest neighbor classifier is proposed for classification. This classifier is used as base/weak classifier for incremental learning algorithm Learn++. A vast range of experimentation is performed on five different databases that demonstrate the incremental learning capability of the proposed system. The experiments using the incrementally learning classification demonstrate promising results.