Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

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Multiagent systems: a modern approach to distributed artificial intelligence

IEEE International Joint Conference on Neural Networks

Stock price modeling and prediction have been challenging objectives for researchers and speculators because of noisy and non-stationary characteristics of samples. With the growth in deep learning, the task of feature learning can be performed more effectively by purposely designed network. In this paper, we propose a novel end-to-end model named multi-filters neural network (MFNN) specifically for feature extraction on financial time series samples and price movement prediction task. Both convolutional and recurrent neurons are integrated to build the multi-filters structure, so that the information from different feature spaces and market views can be obtained. We apply our MFNN for extreme market prediction and signal-based trading simulation tasks on Chinese stock market index CSI 300. Experimental results show that our network outperforms traditional machine learning models, statistical models, and single-structure(convolutional, recurrent, and LSTM) networks in terms of the accuracy, profitability, and stability.

Deep learning-based feature engineering for stock price movement prediction

This letter presents a minimum classification error learning formulation for a single-layer feedforward network (SLFN). By approximating the nonlinear counting step function using a quadratic function, the classification error rate is shown to be deterministically solvable. Essentially the derived solution is related to an existing weighted least-squares method with class-specific weights set according to the size of data set. By considering the class-specific weights as adjustable parameters, the learning formulation extends the classification robustness of the SLFN without sacrificing its intrinsic advantage of being a closed-form algorithm. While the method is applicable to other linear formulations, our empirical results indicate SLFN's effectiveness on classification generalization.

Deterministic neural classification

Backpropagation, similar to most learning algorithms that can form complex decision surfaces, is prone to overfitting. This work presents classification-based objective functions, an approach to training artificial neural networks on classification problems. Classification-based learning attempts to guide the network directly to correct pattern classification rather than using common error minimization heuristics, such as sum-squared error (SSE) and cross-entropy (CE), that do not explicitly minimize classification error. CB1 is presented here as a novel objective function for learning classification problems. It seeks to directly minimize classification error by backpropagating error only on misclassified patterns from culprit output nodes. CB1 discourages weight saturation and overfitting and achieves higher accuracy on classification problems than optimizing SSE or CE. Experiments on a large OCR data set have shown CB1 to significantly increase generalization accuracy over SSE or CE optimization, from 97.86% and 98.10%, respectively, to 99.11%. Comparable results are achieved over several data sets from the UC Irvine Machine Learning Database Repository, with an average increase in accuracy from 90.7% and 91.3% using optimized SSE and CE networks, respectively, to 92.1% for CB1. Analysis indicates that CB1 performs a fundamentally different search of the feature space than optimizing SSE or CE and produces significantly different solutions.

/pdf/classification-based-objective-functions-4g2a7a2eux.pdf

Classification-based objective functions

Effective backpropagation training of multi-layer perceptrons depends on the incorporation of an appropriate error or objective function. Classification-Based (CB) error functions are heuristic approaches that attempt to guide the network directly to correct pattern classification rather than using common error minimization heuristics, such as Sum-Squared Error (SSE) and Cross-Entropy (CE), which do not explicitly minimize classification error. This work presents CB3, a novel CB approach that learns the error function to be used while training. This is accomplished by learning pattern confidence margins during training, which are used to dynamically set output target values for each training pattern. On 11 applications, CB3 significantly outperforms previous CB error functions, and also reduces average test error over conventional error metrics using 0---1 targets without weight decay by 1.8%, and by 1.3% over metrics with weight decay. The CB3 also exhibits lower model variance and tighter mean confidence interval.

/pdf/cb3-an-adaptive-error-function-for-backpropagation-training-1y7n339ce9.pdf

CB3: An Adaptive Error Function for Backpropagation Training

Multi-layer backpropagation, like many learning algorithms that can create complex decision surfaces, is prone to overfitting. Softprop is a novel learning approach presented here that is reminiscent of the softmax explore-exploit Q-learning search heuristic. It fits the problem while delaying settling into error minima to achieve better generalization and more robust learning. This is accomplished by blending standard SSE optimization with lazy training, a new objective function well suited to learning classification tasks, to form a more stable learning model. Over several machine learning data sets, softprop reduces classification error by 17.1 percent and the variance in results by 38.6 percent over standard SSE minimization.

/pdf/softprop-softmax-neural-network-backpropagation-learning-1f1sp447h1.pdf

Softprop: softmax neural network backpropagation learning

Multilayer backpropagation, like most learning algorithms that can create complex decision surfaces, is prone to overfitting. We present a novel approach, called lazy training, for reducing the overfit in multiple-layer networks. Lazy training consistently reduces generalization error of optimized neural networks by more than half on a large OCR dataset and on several real world problems from the UCI machine learning database repository. Here, lazy training is shown to be effective in a multilayered adaptive learning system, reducing the error of an optimized backpropagation network in a speech recognition system by 50.0% on the TIDIGITS corpus.

/pdf/improving-speech-recognition-learning-through-lazy-training-3qqfumch4n.pdf

Improving speech recognition learning through lazy training

Often, the best artificial neural network to solve a real-world problem is relatively complex. However, with the growing popularity of smaller computing devices (hand-held computers, cellular telephones, automobile interfaces, etc.), there is a need for simpler models with comparable accuracy. This paper presents evidence that using a larger model as an oracle to train a smaller model on unlabeled data results in (1) a simpler acceptable model and (2) improved results over standard training methods on a similarly-sized smaller model. On automated spoken-digit recognition, oracle learning resulted in an artificial neural network of half the size that (1) maintained comparable accuracy to the larger neural network and (2) obtained up to a 25% decrease in error over standard training methods.

/pdf/network-simplification-through-oracle-learning-4rvoh3178z.pdf

Network simplification through oracle learning

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