node_id int64 0 76.9k | label int64 0 39 | text stringlengths 13 124k | neighbors listlengths 0 3.32k | mask stringclasses 4
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|---|---|---|---|---|
1,394 | 2 | Title: Vapnik-Chervonenkis entropy of the spherical perceptron
Abstract: Perceptron learning of randomly labeled patterns is analyzed using a Gibbs distribution on the set of realizable labelings of the patterns. The entropy of this distribution is an extension of the Vapnik-Chervonenkis (VC) entropy, reducing to it ... | [
967
] | Validation |
1,395 | 1 | Title: Learning Where To Go without Knowing Where That Is: The Acquisition of a Non-reactive Mobot
Abstract: In the path-imitation task, one agent traces out a path through a second agent's sensory field. The second agent then has to reproduce that path exactly, i.e. move through the sequence of locations visited by th... | [
1541
] | Test |
1,396 | 1 | Title: Evolving a Generalised Behaviour: Artificial Ant Problem Revisited
Abstract: This research aims to demonstrate that a solution for artificial ant problem [4] is very likely to be non-general and relying on the specific characteristics of the Santa Fe trail. It then presents a consistent method which promotes p... | [
970
] | Train |
1,397 | 3 | Title: Speech Recognition with Dynamic Bayesian Networks
Abstract: Dynamic Bayesian networks (DBNs) are a useful tool for representing complex stochastic processes. Recent developments in inference and learning in DBNs allow their use in real-world applications. In this paper, we apply DBNs to the problem of speech r... | [
905,
1287,
1393
] | Train |
1,398 | 2 | Title: Self-Organizing Sets of Experts
Abstract: We describe and evaluate multi-network connectionist systems composed of "expert" networks. By preprocessing training data with a competitive learning network, the system automatically organizes the process of decomposition into expert subtasks. Using several different... | [
1383
] | Test |
1,399 | 2 | Title: Parametric regression 1.1 Learning problem model f bw b w in turn is an estimator
Abstract: Let us present briefly the learning problem we will address in this chapter and the following. The ultimate goal is the modelling of a mapping f : x 7! y from multidimensional input x to output y. The output can be multi-... | [
427,
1463
] | Train |
1,400 | 6 | Title: Towards Robust Model Selection using Estimation and Approximation Error Bounds
Abstract: Let us present briefly the learning problem we will address in this chapter and the following. The ultimate goal is the modelling of a mapping f : x 7! y from multidimensional input x to output y. The output can be multi-d... | [
848,
967
] | Test |
1,401 | 0 | Title: Using Case-Based Reasoning as a Reinforcement Learning Framework for Optimization with Changing Criteria
Abstract: Practical optimization problems such as job-shop scheduling often involve optimization criteria that change over time. Repair-based frameworks have been identified as flexible computational paradi... | [
562,
565,
951,
1554,
2605
] | Validation |
1,402 | 2 | Title: A model f w depending on a set of parameters w is used to estimate
Abstract: We have introduced earlier the use of regularisation in the learning procedure. It should now be understood that regularisation is most often a necessity to increase the quality of the results. Even when the unregularised solution is ac... | [
847
] | Validation |
1,403 | 3 | Title: DART/HYESS Users Guide recursive covering approach to local learning
Abstract: | [
1112
] | Train |
1,404 | 1 | Title: A Hybrid GP/GA Approach for Co-evolving Controllers and Robot Bodies to Achieve Fitness-Specified Tasks
Abstract: Evolutionary approaches have been advocated to automate robot design. Some research work has shown the success of evolving controllers for the robots by genetic approaches. As we can observe, howev... | [
163,
219,
755,
1143
] | Validation |
1,405 | 2 | Title: TH presentee par STATISTICAL LEARNING AND REGULARISATION FOR REGRESSION Application to system identification and time
Abstract: Evolutionary approaches have been advocated to automate robot design. Some research work has shown the success of evolving controllers for the robots by genetic approaches. As we can ob... | [
427,
1463
] | Train |
1,406 | 2 | Title: 82 Lag-space estimation in time-series modelling keep track of cases where the estimation of P
Abstract: When m = 0 (no delays), we set A 0 (ffi) = f(j; k) ; j 6= kg, such that P m (*jffi) depends only on *. The estimated probabilities above become quite noisy when the number of elements in set A m and B m are s... | [
1463
] | Train |
1,407 | 0 | Title: ABSTRACTION CONSIDERED HARMFUL: LAZY LEARNING OF LANGUAGE PROCESSING
Abstract: When m = 0 (no delays), we set A 0 (ffi) = f(j; k) ; j 6= kg, such that P m (*jffi) depends only on *. The estimated probabilities above become quite noisy when the number of elements in set A m and B m are small. For this reason, w... | [
783,
862,
1155,
1626,
1812
] | Validation |
1,408 | 1 | Title: Use of Architecture-Altering Operations to Dynamically Adapt a Three-Way Analog Source Identification Circuit to Accommodate
Abstract: We used genetic programming to evolve b o t h the topology and the sizing (numerical values) for each component of an analog electrical circuit that can correctly classify an inc... | [
1249,
1921,
1931
] | Validation |
1,409 | 1 | Title: Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming
Abstract: An essential component of an intelligent agent is the ability to observe, encode, and use information about its environment. Traditional approaches to Genetic Programming have focused on evolving functional or reactive p... | [
129,
789,
958,
1797,
2220,
2600,
2703
] | Test |
1,410 | 1 | Title: Genetic Algorithm Programming Environments
Abstract: Interest in Genetic algorithms is expanding rapidly. This paper reviews software environments for programming Genetic Algorithms ( GA s). As background, we initially preview genetic algorithms' models and their programming. Next we classify GA software envir... | [
163,
1153
] | Validation |
1,411 | 2 | Title: Connection Pruning with Static and Adaptive Pruning Schedules
Abstract: Neural network pruning methods on the level of individual network parameters (e.g. connection weights) can improve generalization, as is shown in this empirical study. However, an open problem in the pruning methods known today (e.g. OBD, ... | [
881,
1203,
2203
] | Train |
1,412 | 0 | Title: EXPLORING A FRAMEWORK FOR INSTANCE BASED LEARNING AND NAIVE BAYESIAN CLASSIFIERS
Abstract: The relative performance of different methods for classifier learning varies across domains. Some recent Instance Based Learning (IBL) methods, such as IB1-MVDM* 10 , use similarity measures based on conditional class pr... | [
1111,
1328,
1431
] | Train |
1,413 | 0 | Title: References Automatic student modeling and bug library construction using theory refinement. Ph.D. ml/ Symbolic revision
Abstract: ASSERT demonstrates how theory refinement techniques developed in machine learning can be used to ef fec-tively build student models for intelligent tutoring systems. This application... | [
136,
1102
] | Test |
1,414 | 3 | Title: Tractable Inference for Complex Stochastic Processes
Abstract: The monitoring and control of any dynamic system depends crucially on the ability to reason about its current status and its future trajectory. In the case of a stochastic system, these tasks typically involve the use of a belief statea probability... | [
788,
945,
1268,
1287,
1288,
1393
] | Train |
1,415 | 3 | Title: A New Approach for Induction: From a Non-Axiomatic Logical Point of View
Abstract: Non-Axiomatic Reasoning System (NARS) is designed to be a general-purpose intelligent reasoning system, which is adaptive and works under insufficient knowledge and resources. This paper focuses on the components of NARS that co... | [
1504,
1506,
1525
] | Validation |
1,416 | 0 | Title: Synergy and Commonality in Case-Based and Constraint-Based Reasoning
Abstract: Although Case-Based Reasoning (CBR) is a natural formulation for many problems, our previous work on CBR as applied to design made it apparent that there were elements of the CBR paradigm that prevented it from being more widely app... | [
922,
923
] | Test |
1,417 | 2 | Title: Ridge Regression Learning Algorithm in Dual Variables
Abstract: In this paper we study a dual version of the Ridge Regression procedure. It allows us to perform non-linear regression by constructing a linear regression function in a high dimensional feature space. The feature space representation can result in... | [
1421
] | Train |
1,418 | 2 | Title: BCM Network develops Orientation Selectivity and Ocular Dominance in Natural Scene Environment.
Abstract: A two-eye visual environment is used in training a network of BCM neurons. We study the effect of misalignment between the synaptic density functions from the two eyes, on the formation of orientation sele... | [
726,
989,
2499
] | Train |
1,419 | 3 | Title: BAYESIAN ESTIMATION OF THE VON MISES CONCENTRATION PARAMETER
Abstract: The von Mises distribution is a maximum entropy distribution. It corresponds to the distribution of an angle of a compass needle in a uniform magnetic field of direction, , with concentration parameter, . The concentration parameter, , is t... | [
525,
1550
] | Validation |
1,420 | 0 | Title: Design, Analogy, and Creativity
Abstract: : | [
1047,
1138,
1354
] | Validation |
1,421 | 2 | Title: Support Vector Machines, Reproducing Kernel Hilbert Spaces and the Randomized GACV 1
Abstract: 1 Prepared for the NIPS 97 Workshop on Support Vector Machines. Research sponsored in part by NSF under Grant DMS-9704758 and in part by NEI under Grant R01 EY09946. This is a second revised and corrected version of ... | [
821,
1417
] | Train |
1,422 | 2 | Title: Generating Accurate and Diverse Members of a Neural-Network Ensemble
Abstract: Neural-network ensembles have been shown to be very accurate classification techniques. Previous work has shown that an effective ensemble should consist of networks that are not only highly correct, but ones that make their errors ... | [
550,
826,
828,
1223,
1237,
1273,
1657
] | Train |
1,423 | 2 | Title: Comparison of Regression Methods, Symbolic Induction Methods and Neural Networks in Morbidity Diagnosis and Mortality
Abstract: Classifier induction algorithms differ on what inductive hypotheses they can represent, and on how they search their space of hypotheses. No classifier is better than another for all pr... | [
1328,
2583
] | Train |
1,424 | 1 | Title: Multi-parent's niche: n-ary crossovers on NK-landscapes
Abstract: Using the multi-parent diagonal and scanning crossover in GAs reproduction operators obtain an adjustable arity. Hereby sexuality becomes a graded feature instead of a Boolean one. Our main objective is to relate the performance of GAs to the ex... | [
1218,
1530,
1799
] | Train |
1,425 | 3 | Title: Unsupervised Learning Using MML
Abstract: This paper discusses the unsupervised learning problem. An important part of the unsupervised learning problem is determining the number of constituent groups (components or classes) which best describes some data. We apply the Minimum Message Length (MML) criterion to... | [
525,
684,
1550
] | Test |
1,426 | 0 | Title: REPRESENTING PHYSICAL AND DESIGN KNOWLEDGE IN INNOVATIVE DESIGN
Abstract: This paper discusses the unsupervised learning problem. An important part of the unsupervised learning problem is determining the number of constituent groups (components or classes) which best describes some data. We apply the Minimum M... | [
1354
] | Train |
1,427 | 3 | Title: SINGLE FACTOR ANALYSIS BY MML ESTIMATION
Abstract: The Minimum Message Length (MML) technique is applied to the problem of estimating the parameters of a multivariate Gaussian model in which the correlation structure is modelled by a single common factor. Implicit estimator equations are derived and compared w... | [
525,
1550
] | Train |
1,428 | 5 | Title: Inverse entailment and Progol
Abstract: This paper firstly provides a re-appraisal of the development of techniques for inverting deduction, secondly introduces Mode-Directed Inverse Entailment (MDIE) as a generalisation and enhancement of previous approaches and thirdly describes an implementation of MDIE in ... | [
1312,
1601,
1622,
2079,
2229,
2329,
2424,
2493,
2539,
2617
] | Train |
1,429 | 5 | Title: Learning First-Order Definitions of Functions
Abstract: First-order learning involves finding a clause-form definition of a relation from examples of the relation and relevant background information. In this paper, a particular first-order learning system is modified to customize it for finding definitions of ... | [
675,
1208,
1601
] | Test |
1,430 | 2 | Title: Adaptive Boosting of Neural Networks for Character Recognition
Abstract: Technical Report #1072, D epartement d'Informatique et Recherche Op erationnelle, Universit e de Montr eal Abstract Boosting is a general method for improving the performance of any learning algorithm that consistently generates classifie... | [
569,
1356,
1484
] | Validation |
1,431 | 2 | Title: Learning to Represent Codons: A Challenge Problem for Constructive Induction
Abstract: The ability of an inductive learning system to find a good solution to a given problem is dependent upon the representation used for the features of the problem. Systems that perform constructive induction are able to change... | [
698,
861,
1181,
1412
] | Train |
1,432 | 1 | Title: EVOLUTIONARY ALGORITHMS IN ROBOTICS
Abstract: | [
910,
964,
965,
1573
] | Train |
1,433 | 6 | Title: Learning Boxes in High Dimension
Abstract: We present exact learning algorithms that learn several classes of (discrete) boxes in f0; : : :; ` 1g n . In particular we learn: (1) The class of unions of O(log n) boxes in time poly(n; log `) (solving an open problem of [16, 12]; in [3] this class is shown to be l... | [
798,
1026,
1095
] | Validation |
1,434 | 5 | Title: Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming
Abstract: This paper demonstrates the capabilities of Foidl, an inductive logic programming (ILP) system whose distinguishing characteristics are the ability to produce first-order decision lists, the use of an output completene... | [
1208
] | Test |
1,435 | 2 | Title: Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming
Abstract: Further Results on Controllability Abstract This paper studies controllability properties of recurrent neural networks. The new contributions are: (1) an extension of a previous result to a slightly different model, (2... | [
1028,
1042,
1043
] | Test |
1,436 | 3 | Title: Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming
Abstract: Figure 9: Results for various optimizations. Figure 10: Results with and without markov boundary scoring. | [
895
] | Train |
1,437 | 3 | Title: Coupled hidden Markov models for complex action recognition
Abstract: c flMIT Media Lab Perceptual Computing / Learning and Common Sense Technical Report 407 20nov96 Abstract We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superi... | [
787,
891,
1287,
1393,
1593
] | Validation |
1,438 | 4 | Title: Learning from undiscounted delayed rewards
Abstract: The general framework of reinforcement learning has been proposed by several researchers for both the solution of optimization problems and the realization of adaptive control schemes. To allow for an efficient application of reinforcement learning in either... | [
294,
565,
807
] | Test |
1,439 | 1 | Title: Minimum-Perimeter Domain Assignment
Abstract: For certain classes of problems defined over two-dimensional domains with grid structure, optimization problems involving the assignment of grid cells to processors present a nonlinear network model for the problem of partitioning tasks among processors so as to mi... | [
53,
803
] | Validation |
1,440 | 4 | Title: Value Function Approximations and Job-Shop Scheduling
Abstract: We report a successful application of TD() with value function approximation to the task of job-shop scheduling. Our scheduling problems are based on the problem of scheduling payload processing steps for the NASA space shuttle program. The value ... | [
82,
565,
1378
] | Validation |
1,441 | 1 | Title: Nonlinearity, Hyperplane Ranking and the Simple Genetic Algorithm
Abstract: Several metrics are used in empirical studies to explore the mechanisms of convergence of genetic algorithms. The metric is designed to measure the consistency of an arbitrary ranking of hyperplanes in a partition with respect to a tar... | [
941,
1638,
1717
] | Test |
1,442 | 5 | Title: Learning Goal-Decomposition Rules using Exercises
Abstract: Exercises are problems ordered in increasing order of difficulty. Teaching problem-solving through exercises is a widely used pedagogic technique. A computational reason for this is that the knowledge gained by solving simple problems is useful in eff... | [
344,
414,
675,
1135,
1444,
1445
] | Validation |
1,443 | 4 | Title: Residual Q-Learning Applied to Visual Attention
Abstract: Foveal vision features imagers with graded acuity coupled with context sensitive sensor gaze control, analogous to that prevalent throughout vertebrate vision. Foveal vision operates more efficiently than uniform acuity vision because resolution is trea... | [
1540
] | Test |
1,444 | 6 | Title: Learning Horn Definitions with Equivalence and Membership Queries
Abstract: A Horn definition is a set of Horn clauses with the same head literal. In this paper, we consider learning non-recursive, function-free first-order Horn definitions. We show that this class is exactly learnable from equivalence and mem... | [
1135,
1442
] | Train |
1,445 | 5 | Title: Theory-guided Empirical Speedup Learning of Goal Decomposition Rules
Abstract: Speedup learning is the study of improving the problem-solving performance with experience and from outside guidance. We describe here a system that successfully combines the best features of Explanation-based learning and empirical... | [
344,
414,
675,
1442
] | Test |
1,446 | 2 | Title: STABILIZATION WITH SATURATED ACTUATORS, A WORKED EXAMPLE:F-8 LONGITUDINAL FLIGHT CONTROL
Abstract: The authors and coworkers recently proved general theorems on the global stabilization of linear systems subject to control saturation. This paper develops in detail an explicit design for the linearized equation... | [
948,
1282
] | Train |
1,447 | 4 | Title: Model of the Environment to Avoid Local Learning
Abstract: Pier Luca Lanzi Technical Report N. 97.46 December 20 th , 1997 | [
566,
1515,
1581
] | Test |
1,448 | 0 | Title: Automatic Storage and Indexing of Plan Derivations based on Replay Failures
Abstract: When a case-based planner is retrieving a previous case in preparation for solving a new similar problem, it is often not aware of all of the implicit features of the new problem situation which determine if a particular case... | [
1621
] | Train |
1,449 | 6 | Title: Adaptive Mixtures of Probabilistic Transducers
Abstract: We describe and analyze a mixture model for supervised learning of probabilistic transducers. We devise an on-line learning algorithm that efficiently infers the structure and estimates the parameters of each probabilistic transducer in the mixture. Theo... | [
453,
1025
] | Train |
1,450 | 2 | Title: Plasticity-Mediated Competitive Learning
Abstract: Differentiation between the nodes of a competitive learning network is conventionally achieved through competition on the basis of neural activity. Simple inhibitory mechanisms are limited to sparse representations, while decorrelation and factorization scheme... | [
731,
808,
1710
] | Train |
1,451 | 2 | Title: On the Computation of the Induced L 2 Norm of Single Input Linear Systems with Saturation
Abstract: Differentiation between the nodes of a competitive learning network is conventionally achieved through competition on the basis of neural activity. Simple inhibitory mechanisms are limited to sparse representati... | [
1272,
1281,
1346,
1604
] | Train |
1,452 | 2 | Title: Bayesian Training of Backpropagation Networks by the Hybrid Monte Carlo Method
Abstract: It is shown that Bayesian training of backpropagation neural networks can feasibly be performed by the "Hybrid Monte Carlo" method. This approach allows the true predictive distribution for a test case given a set of train... | [
157,
560,
1289,
1375
] | Train |
1,453 | 0 | Title: Automatic Indexing, Retrieval and Reuse of Topologies in Architectual Layouts
Abstract: Former layouts contain much of the know-how of architects. A generic and automatic way to formalize this know-how in order to use it by a computer would save a lot of effort and money. However, there seems to be no such way... | [
539,
1152,
1210
] | Train |
1,454 | 2 | Title: A Neural Network Model for Prognostic Prediction
Abstract: An important and difficult prediction task in many domains, particularly medical decision making, is that of prognosis. Prognosis presents a unique set of problems to a learning system when some of the outputs are unknown. This paper presents a new app... | [
524,
1169
] | Train |
1,455 | 1 | Title: Self-Adaptation in Genetic Algorithms of external parameters of a GA is seen as a first
Abstract: In this paper a new approach is presented, which transfers a basic idea from Evolution Strategies (ESs) to GAs. Mutation rates are changed into endogeneous items which are adapting during the search process. First e... | [
793,
1069,
1153,
1685,
1694
] | Train |
1,456 | 6 | Title: An Interactive Model of Teaching
Abstract: Previous teaching models in the learning theory community have been batch models. That is, in these models the teacher has generated a single set of helpful examples to present to the learner. In this paper we present an interactive model in which the learner has the ... | [
308,
1095,
1343,
1469
] | Train |
1,457 | 2 | Title: Actively Searching for an Effective Neural-Network Ensemble
Abstract: A neural-network ensemble is a very successful technique where the outputs of a set of separately trained neural network are combined to form one unified prediction. An effective ensemble should consist of a set of networks that are not only... | [
163,
569,
826,
828,
1237,
1462
] | Train |
1,458 | 3 | Title: Sequential Thresholds: Context Sensitive Default Extensions
Abstract: Default logic encounters some conceptual difficulties in representing common sense reasoning tasks. We argue that we should not try to formulate modular default rules that are presumed to work in all or most circumstances. We need to take in... | [
838,
1714
] | Train |
1,459 | 4 | Title: Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms
Abstract: The problem of maximizing the expected total discounted reward in a completely observable Markovian environment, i.e., a Markov decision process (mdp), models a particular class of sequential decision pro... | [
45,
57,
167,
210,
473,
566,
633,
671,
749,
775,
804,
1137,
1540,
1687,
2078,
2221,
2404
] | Train |
1,460 | 6 | Title: Learning from Queries and Examples with Tree-structured Bias
Abstract: Incorporating declarative bias or prior knowledge into learning is an active research topic in machine learning. Tree-structured bias specifies the prior knowledge as a tree of "relevance" relationships between attributes. This paper presen... | [
638,
672,
924
] | Train |
1,461 | 2 | Title: Learning in Boltzmann Trees
Abstract: We introduce a large family of Boltzmann machines that can be trained using standard gradient descent. The networks can have one or more layers of hidden units, with tree-like connectivity. We show how to implement the supervised learning algorithm for these Boltzmann mach... | [
304,
954,
1288,
1357,
1593
] | Test |
1,462 | 2 | Title: Learning from Bad Data
Abstract: The data describing resolutions to telephone network local loop "troubles," from which we wish to learn rules for dispatching technicians, are notoriously unreliable. Anecdotes abound detailing reasons why a resolution entered by a technician would not be valid, ranging from sy... | [
1457,
1657
] | Train |
1,463 | 3 | Title: Bias, variance and prediction error for classification rules
Abstract: We study the notions of bias and variance for classification rules. Following Efron (1978) we develop a decomposition of prediction error into its natural components. Then we derive bootstrap estimates of these components and illustrate how... | [
931,
999,
1053,
1361,
1399,
1405,
1406,
1512
] | Train |
1,464 | 2 | Title: LINEAR SYSTEMS WITH SIGN-OBSERVATIONS
Abstract: This paper deals with systems that are obtained from linear time-invariant continuous-or discrete-time devices followed by a function that just provides the sign of each output. Such systems appear naturally in the study of quantized observations as well as in si... | [
200,
1021,
1100,
1254,
1470
] | Train |
1,465 | 0 | Title: REPRO: Supporting Flowsheet Design by Case-Base Retrieval
Abstract: Case-Based Reasoning (CBR) paradigm is very close to the designer behavior during the conceptual design, and seems to be a fruitable computer aided-design approach if a library of design cases is available. The goal of this paper is to present... | [
1354
] | Train |
1,466 | 1 | Title: A STUDY OF CROSSOVER OPERATORS IN GENETIC PROGRAMMING
Abstract: Holland's analysis of the sources of power of genetic algorithms has served as guidance for the applications of genetic algorithms for more than 15 years. The technique of applying a recombination operator (crossover) to a population of individual... | [
728,
943,
1016,
1650
] | Test |
1,467 | 1 | Title: Adaptive Strategy Selection for Concept Learning
Abstract: In this paper, we explore the use of genetic algorithms (GAs) to construct a system called GABIL that continually learns and refines concept classification rules from its interac - tion with the environment. The performance of this system is compared w... | [
163,
793,
1333
] | Train |
1,468 | 6 | Title: Preventing "Overfitting" of Cross-Validation Data
Abstract: Suppose that, for a learning task, we have to select one hypothesis out of a set of hypotheses (that may, for example, have been generated by multiple applications of a randomized learning algorithm). A common approach is to evaluate each hypothesis i... | [
638,
847,
848
] | Test |
1,469 | 6 | Title: Warning: missing six few referencesfixed in proceedings. Learning with Queries but Incomplete Information (Extended Abstract)
Abstract: We investigate learning with membership and equivalence queries assuming that the information provided to the learner is incomplete. By incomplete we mean that some of the mem... | [
1003,
1004,
1364,
1456,
1661,
1705
] | Test |
1,470 | 2 | Title: Interconnected Automata and Linear Systems: A Theoretical Framework in Discrete-Time In Hybrid Systems III: Verification
Abstract: This paper summarizes the definitions and several of the main results of an approach to hybrid systems, which combines finite automata and linear systems, developed by the author in ... | [
411,
1037,
1464
] | Test |
1,471 | 2 | Title: New Characterizations of Input to State Stability
Abstract: We present new characterizations of the Input to State Stability property. As a consequence of these results, we show the equivalence between the ISS property and several (apparent) variations proposed in the literature. | [
447,
693,
1281,
1282,
1501,
1633
] | Train |
1,472 | 2 | Title: A SUCCESSIVE LINEAR PROGRAMMING APPROACH FOR INITIALIZATION AND REINITIALIZATION AFTER DISCONTINUITIES OF DIFFERENTIAL ALGEBRAIC EQUATIONS
Abstract: Determination of consistent initial conditions is an important aspect of the solution of differential algebraic equations (DAEs). Specification of inconsistent in... | [
878
] | Test |
1,473 | 1 | Title: Evolving Non-Determinism: An Inventive and Efficient Tool for Optimization and Discovery of Strategies
Abstract: In the field of optimization and machine learning techniques, some very efficient and promising tools like Genetic Algorithms (GAs) and Hill-Climbing have been designed. In this same field, the Evol... | [
380,
1249,
1474
] | Train |
1,474 | 1 | Title: Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies
Abstract: In the field of optimization and machine learning techniques, some very efficient and promising tools like Genetic Algorithms (GAs) and Hill-Climbing have been designed. In this same field, the Evolving... | [
380,
1249,
1473,
1728
] | Train |
1,475 | 0 | Title: Within the Letter of the Law: open-textured planning
Abstract: Most case-based reasoning systems have used a single "best" or "most similar" case as the basis for a solution. For many problems, however, there is no single exact solution. Rather, there is a range of acceptable answers. We use cases not only as ... | [
801,
1642
] | Validation |
1,476 | 1 | Title: Program Optimization for Faster Genetic Programming
Abstract: We have used genetic programming to develop efficient image processing software. The ultimate goal of our work is to detect certain signs of breast cancer that cannot be detected with current segmentation and classification methods. Traditional tech... | [
1178,
1277
] | Train |
1,477 | 2 | Title: Two Constructive Methods for Designing Compact Feedforward Networks of Threshold Units
Abstract: We propose two algorithms for constructing and training compact feedforward networks of linear threshold units. The Shift procedure constructs networks with a single hidden layer while the PTI constructs multilayer... | [
1252
] | Validation |
1,478 | 3 | Title: Lazy Bayesian Trees
Abstract: The naive Bayesian classifier is simple and effective, but its attribute independence assumption is often violated in the real world. A number of approaches have been developed that seek to alleviate this problem. A Bayesian tree learning algorithm builds a decision tree, and gene... | [
1335,
1336,
2338
] | Train |
1,479 | 3 | Title: Revising Bayesian Network Parameters Using Backpropagation
Abstract: The problem of learning Bayesian networks with hidden variables is known to be a hard problem. Even the simpler task of learning just the conditional probabilities on a Bayesian network with hidden variables is hard. In this paper, we present... | [
136,
1102,
2017,
2543
] | Test |
1,480 | 2 | Title: Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration
Abstract: Computational models of natural systems often contain free parameters that must be set to optimize the predictive accuracy of the models. This process|called calibration|can be viewed as a form of supervised learning in... | [
924,
1532
] | Test |
1,481 | 1 | Title: An Evolutionary Approach to Learning in Robots
Abstract: Evolutionary learning methods have been found to be useful in several areas in the development of intelligent robots. In the approach described here, evolutionary algorithms are used to explore alternative robot behaviors within a simulation model as a w... | [
910,
964,
965,
966,
1573
] | Validation |
1,482 | 6 | Title: Generalizations of the Bias/Variance Decomposition for Prediction Error
Abstract: The bias and variance of a real valued random variable, using squared error loss, are well understood. However because of recent developments in classification techniques it has become desirable to extend these concepts to genera... | [
1484
] | Train |
1,483 | 0 | Title: Context-Based Similarity Applied to Retrieval of Relevant Cases
Abstract: Retrieving relevant cases is a crucial component of case-based reasoning systems. The task is to use user-defined query to retrieve useful information, i.e., exact matches or partial matches which are close to query-defined request accor... | [
857,
1123,
1125,
1498,
2052,
2060
] | Test |
1,484 | 6 | Title: Experiments with a New Boosting Algorithm
Abstract: In an earlier paper, we introduced a new boosting algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random ... | [
822,
931,
1057,
1092,
1197,
1220,
1237,
1430,
1482,
1500,
1521,
1522,
1692
] | Train |
1,485 | 2 | Title: Maximizing the Robustness of a Linear Threshold Classifier with Discrete Weights
Abstract: Quantization of the parameters of a Perceptron is a central problem in hardware implementation of neural networks using a numerical technology. An interesting property of neural networks used as classifiers is their abil... | [
1159,
1252
] | Train |
1,486 | 5 | Title: Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries
Abstract: Machine learning techniques can be used to extract knowledge from data stored in medical databases. In our application, various machine learning algorithms were used to extract diagnostic knowledge to suppo... | [
426,
1008,
1569
] | Train |
1,487 | 2 | Title: A Divide-and-Conquer Approach to Learning from Prior Knowledge
Abstract: This paper introduces a new machine learning task|model calibration|and presents a method for solving a particularly difficult model calibration task that arose as part of a global climate change research project. The model calibration ta... | [
1528,
1532
] | Train |
1,488 | 2 | Title: Identification and Control of Nonlinear Systems Using Neural Network Models: Design and Stability Analysis
Abstract: Report 91-09-01 September 1991 (revised) May 1994 | [
206,
427,
611,
980,
1490,
1668
] | Test |
1,489 | 5 | Title: Dlab: A Declarative Language Bias Formalism
Abstract: We describe the principles and functionalities of Dlab (Declarative LAnguage Bias). Dlab can be used in inductive learning systems to define syntactically and traverse efficiently finite subspaces of first order clausal logic, be it a set of propositional f... | [
177,
837
] | Train |
1,490 | 2 | Title: FEEDBACK STABILIZATION USING TWO-HIDDEN-LAYER NETS
Abstract: This paper compares the representational capabilities of one hidden layer and two hidden layer nets consisting of feedforward interconnections of linear threshold units. It is remarked that for certain problems two hidden layers are required, contrar... | [
206,
531,
1488
] | Train |
1,491 | 6 | Title: Discovery as Autonomous Learning from the Environment
Abstract: Discovery involves collaboration among many intelligent activities. However, little is known about how and in what form such collaboration occurs. In this paper, a framework is proposed for autonomous systems that learn and discover from their env... | [
851,
903,
1390,
1605
] | Train |
1,492 | 6 | Title: Predicting Time Series with Support Vector Machines
Abstract: Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different cost functions for Support Vectors: training with (i) an * insensitive loss and (ii) Huber's robust loss functio... | [
1050,
1724
] | Train |
1,493 | 2 | Title: Evaluation of Pattern Classifiers for Fingerprint and OCR Applications
Abstract: In this paper we evaluate the classification accuracy of four statistical and three neural network classifiers for two image based pattern classification problems. These are fingerprint classification and optical character recogni... | [
611,
774,
867,
1732
] | Train |
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