Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

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Reinforcement learning in robotics: A survey

Reinforcement Learning in Robotics: A Survey.

We develop a novel maximum neighborhood margin discriminant projection (MNMDP) technique for dimensionality reduction of high-dimensional data. It utilizes both the local information and class information to model the intraclass and interclass neighborhood scatters. By maximizing the margin between intraclass and interclass neighborhoods of all points, MNMDP cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes. To verify the classification performance of the proposed MNMDP, it is applied to the PolyU HRF and FKP databases, the AR face database, and the UCI Musk database, in comparison with the competing methods such as PCA and LDA. The experimental results demonstrate the effectiveness of our MNMDP in pattern classification.

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Maximum neighborhood margin discriminant projection for classification

In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in real-world problems are discussed.

Text Classification Algorithms: A Survey

Kernel class-wise locality preserving projection

Sparse representation-based MRI super-resolution reconstruction

When localizing the position of an unknown node for wireless sensor networks, the received signal strength indicator (RSSI) value is usually considered to fit a fixed attenuation model with a corresponding communication distance. However, due to some negative factors, the relationship is not valid in the actual localization environment, which leads to a considerable localization error. Therefore, we present a method for improved RSSI-based localization through uncertain data mapping. Starting from an advanced RSSI measurement, the distributions of the RSSI data tuples are determined and expressed in terms of interval data. Then, a data tuple pattern matching strategy is applied to the RSSI data vector during the localization procedure. Experimental results in three representative wireless environments show the feasibility and effectiveness of the proposed approach.

RSSI-Based Localization Through Uncertain Data Mapping for Wireless Sensor Networks

For mobile communication traffic series, an accurate multistep prediction result plays an important role in network management, capacity planning, traffic congestion control, channel equalization, etc. A novel time series forecasting based on echo state networks and multiplicative seasonal ARIMA model are proposed for this multiperiodic, nonstationary, mobile communication traffic series. Motivated by the fact that the real traffic series exhibits periodicities at the cycle of 6, 12, and 24 h, as well as 1 week, we isolate most of mentioned above features for each cell and integrate all the wavelet multiresolution sublayers into two parts for consideration of alleviating the accumulated error. On seasonal characters, multiplicative seasonal ARIMA model is to predict the seasonal part, and echo state networks are to deal with the smooth part because of its prominent approximation capabilities and convenience. Experimental results on real traffic dataset show that proposed method performs well on the prediction accuracy.

Jun-Bao Li

Papers

Kernel class-wise locality preserving projection

Sparse representation-based MRI super-resolution reconstruction

RSSI-Based Localization Through Uncertain Data Mapping for Wireless Sensor Networks

A novel hybridization of echo state networks and multiplicative seasonal ARIMA model for mobile communication traffic series forecasting

Adaptive quasiconformal kernel discriminant analysis