ChordNet: Learning and Producing Voice Leading with Neural Networks and Dynamic Programming

A hybrid threshold curve model for optimal yield management: neural networks and dynamic programming

In domains in which single agent learning is a more natural metaphor for an artifact-embedded agent, Exemplar-Based Learning (EBL) requires significantly large sets of training examples for it to be applicable. Obviously large sets of training examples contradict resource capabilities of artifacts. To make EBL a possibility for these artifacts, sets of training examples must be reduced in size in a way that does not compromise learning performance in order to relieve artifacts' resources (e.g. memory). In this paper, we investigate training sets requirements for artifacts learning and propose a ranking-based Stratified Ordered Selection (SOS) method to scale them down. Contrary to reduction approaches in mainstream learning, this method has been designed with resource constraint nature of artifacts in mind. Artifacts shall use an intermediary which implements SOS to, dynamically and on-demand, retrieve training subsets based on their resource capacities (e.g. memory, CPU). SOS uses a new Level Order (LO) ranking scheme which has been designed to broaden representation of classes of examples, to quicken data retrieval, and to allow for retrieval of subsets of varying sizes while ensuring same or near same learning performance. We present how SOS evaluates on various well known machine learning datasets and how it compares to some of the best performing data reduction approaches.

CHAPTER 14 - Representation Generation in an Exploratory Learning System

Neural networks are abstract structures modeled by the brain to store evidence in the form of spikes. When introduced in VLSI circuits, neural networks are supposed to have new computer processing methods and economically viable computer simulations. We suggest a novel set of training examples for neural nets spatial and temporal coding in this article. In just this procedure, going through the roof neuronal is programmed to promote analogue VLSI applications with resistor analogue memory, from which incredible energy consumption can be accomplished. Can also suggest many strategies to boost efficiency on a model training and prove that the proposed method’s SVM classifier is as high as it was for the retrained dataset’s province temporal coding Computational Intelligence algorithms. Incorporating the developed framework can even recommend very massive circuit boards. The frequency analogue circuits utilize intermittent processing to reimburse capacitance processes, unlike the traditional analogue voltage and current type circuitry being used compute-in-memory circuits. Even though connectors lacking operating amps can still be constructed, it can also be controlled with incredibly low energy consumption. Finally, the preservation of the designed highlights algorithms toward alterations from the system’s production phase and is inevitable in analogue VLSI deployment.

This paper introduces a method that improves the performance of a problem solver by reformulating its domain theory into one in which functionally relevant features are explicit in the syntax. This method, in contrast to previous reformulation methods, employs sets of training examples to constrain and direct the reformulation process. The use of examples offers two advantages over purely deductive approaches: First, the examples identify the exact part of the domain theory to be reformulated. Second, a proof with examples is much simpler to construct than a general proof because it is fully instantiated. The method exploits the fact that what is relevant to a goal is syntactically explicit in successful solutions to that goal. The method first takes as input a set of training examples that “exercise” an important part of the domain theory and then applies the problem solver to explain the examples in terms of a relevant goal. Next, the set of explanations is “clustered” into cases and then generalized using the induction over explanations method, forming a set of general explanations. Finally, these general explanations are reformulated into new domain theory rules. We illustrate the method in the domain of chess. We reformulate a simple declarative encoding of legal-move to produce a new domain theory that can generate the legal moves in a tenth of the time required by the original theory. We also show how the reformulated theory can more efficiently describe the important knight-fork feature.

Training a 3-node neural network is NP-complete

Accelerating neural network training using weight extrapolations

Whoso neglects learning in his youth, loses the past and is dead for the future . –Euripides (484 BC – 406 BC), Phrixus, Frag. 927 Learning is the ability of an agent to improve its behavior based on experience. This could mean the following: The range of behaviors is expanded; the agent can do more. The accuracy on tasks is improved; the agent can do things better. The speed is improved; the agent can do things faster. The ability to learn is essential to any intelligent agent. As Euripides pointed, learning involves an agent remembering its past in a way that is useful for its future. This chapter considers supervised learning: given a set of training examples made up of input–output pairs, predict the output of a new input. We show how such learning may be based on one of four possible approaches: choosing a single hypothesis that fits the training examples well, predicting directly from the training examples, selecting the subset of a hypothesis space consistent with the training examples, or finding the posterior probability distribution of hypotheses conditioned on the training examples. Chapter 11 goes beyond supervised learning and considers clustering (often called unsupervised learning), learning probabilistic models, and reinforcement learning. Section 14.2 (page 606) considers learning relational representations.

Who so neglects learning in his youth, loses the past and is dead for the future. – Euripides (484 BC – 406 BC), Phrixus, Frag. 927 Learning is the ability of an agent to improve its behavior based on experience. This could mean the following • The range of behaviors is expanded; the agent can do more. • The accuracy on tasks is improved; the agent can do things better. • The speed is improved; the agent can do things faster. The ability to learn is essential to any intelligent agent. As Euripides pointed out, learning involves an agent remembering its past in a way that is useful for its future. This chapter considers the problem of making a prediction as supervised learning: given a set of training examples made up of input–output pairs, predict the output of a new example where only the inputs are given. We explore four approaches to learning: choosing a single hypothesis that fits the training examples well, predicting directly from the training examples, selecting the subset of a hypothesis space consistent with the training examples, or (in Section 10.4 (page 512)) predicting based on the posterior probability distribution of hypotheses conditioned on the training examples. Chapter 10 considers learning probabilistic models. Chapter 12 covers reinforcement learning. Section 15.2 (page 701) considers learning relational representations. Learning Issues The following components are part of any learning problem: Task The behavior or task that is being improved Data The experiences that are used to improve performance in the task, usually in the form of a sequence of examples Measure of improvement How the improvement is measured – for example, new skills that were not present initially, increasing accuracy in prediction, or improved speed Consider the agent internals of Figure 2.9 (page 66). The problem of learning is to take in prior knowledge and data (e.g., about the experiences of the agent) and to create an internal representation (the knowledge base) that is used by the agent as it acts. Learning techniques face the following issues: Task Virtually any task for which an agent can get data or experiences can be learned.

A mixed-integer programming approach to GRNN parameter estimation

When a Deep Neural Network makes a misprediction, it can be challenging for a developer to understand why. While there are many models for interpretability in terms of predictive features, it may be more natural to isolate a small set of training examples that have the greatest influence on the prediction. However, it is often the case that every training example contributes to a prediction in some way but with varying degrees of responsibility. We present Partition Aware Local Model (PALM), which is a tool that learns and summarizes this responsibility structure to aide machine learning debugging. PALM approximates a complex model (e.g., a deep neural network) using a two-part surrogate model: a meta-model that partitions the training data, and a set of sub-models that approximate the patterns within each partition. These sub-models can be arbitrarily complex to capture intricate local patterns. However, the meta-model is constrained to be a decision tree. This way the user can examine the structure of the meta-model, determine whether the rules match intuition, and link problematic test examples to responsible training data efficiently. Queries to PALM are nearly 30x faster than nearest neighbor queries for identifying relevant data, which is a key property for interactive applications.

We develop a novel framework that aims at automatically adapting previously learned information extraction knowledge from a source Web site to a new unseen target site in the same domain. Two kinds of features related to the text fragments from the Web documents are investigated. The first type of feature is called, a site-invariant feature. These features likely remain unchanged in Web pages from different sites in the same domain. The second type of feature is called a site-dependent feature. These features are different in the Web pages collected from different Web sites, while they are similar in the Web pages originating from the same site. In our framework, we derive the site-invariant features from previously learned extraction knowledge and the items previously collected or extracted from the source Web site. The derived site-invariant features will be exploited to automatically seek a new set of training examples in the new unseen target site. Both the site-dependent features and the site-invariant features of these automatically discovered training examples will be considered in the learning of new information extraction knowledge for the target site. We conducted extensive experiments on a set of real-world Web sites collected from three different domains to demonstrate the performance of our framework. For example, by just providing training examples from one online book catalog Web site, our approach can automatically extract information from ten different book catalog sites achieving an average precision and recall of 71.9% and 84.0% respectively without any further manual intervention.

In this paper we propose an algorithm for the constrained two-dimensional cutting stock problem (TDC) in which a single stock sheet has to be cut into a set of small pieces, while maximizing the value of the pieces cut. The TDC problem is NP-hard in the strong sense and finds many practical applications in the cutting and packing area. The proposed algorithm is a hybrid approach in which a depth-first search using hill-climbing strategies and dynamic programming techniques are combined. The algorithm starts with an initial (feasible) lower bound computed by solving a series of single bounded knapsack problems. In order to enhance the first-level lower bound, we introduce an incremental procedure which is used within a top-down branch-and-bound procedure. We also propose some hill-climbing strategies in order to produce a good trade-off between the computational time and the solution quality. Extensive computational testing on problem instances from the literature shows the effectiveness of the proposed approach. The obtained results are compared to the results published by Alvarez-Valdes et al. (2002).

In the last few years, object detection techniques have progressed immensely. Impressive detection results have been achieved for many objects such as faces [11, 14, 9] and cars [11]. The robustness of these systems emerges from a training stage utilizing thousands of positive examples. One approach to enable learning from a small set of training examples is to find an efficient set of features that accurately represent the target object. Unfortunately, automatically selecting such a feature set is a difficult task in itself. In this paper we present a novel feature selection method that is based on the notion of object categories. We assume that when learning to recognize a new object (like an apple) we also know a category it belongs to (fruit). We further assume that features that are useful for learning other objects in the same category (e.g. pear or orange) will also be useful for learning the novel object. This leads to a simple criterion for selecting features and building classifiers. We show that our method gives significant improvement in detection performance in challenging domains.

Probability estimation from a given set of training examples is crucial for learning Naive Bayes (NB) Classifiers. For an insufficient number of training examples, the estimation will suffer from the zero-frequency problem which does not allow NB classifiers to classify instances whose conditional probabilities are zero. Laplace-estimate and M-estimate are two common methods which alleviate the zero-frequency problem by adding some fixed terms to the probability estimation to avoid zero conditional probability. A major issue with this type of design is that the fixed terms are pre-specified without considering the uniqueness of the underlying training data. In this paper, we propose an Artificial Immune System (AIS) based self-adaptive probability estimation method, namely AISENB, which uses AIS to automatically and self-adaptively select the optimal terms and values for probability estimation. The unique immune system based evolutionary computation process, including initialization, clone, mutation, and crossover, ensure that AISENB can adjust itself to the data without explicit specification of functional or distributional forms for the underlying model. Experimental results and comparisons on 36 benchmark datasets demonstrate that AISENB significantly outperforms traditional probability estimation based Naive Bayes classification approaches.