node_id int64 0 76.9k | label int64 0 39 | text stringlengths 13 124k | neighbors listlengths 0 3.32k | mask stringclasses 4
values |
|---|---|---|---|---|
3,180 | 4 | Visualizing the Structure of the World Wide Web in 3D Hyperbolic Space We visualize the structure of sections of the World Wide Web by constructing graphical representations in 3D hyperbolic space. The felicitous property that hyperbolic space has "more room" than Euclidean space allows more information to be seen amid... | [
2532
] | Train |
3,181 | 3 | VideoGraph: A Graphical Object-based Model for Representing and Querying Video Data . Modeling video data poses a great challenge since they do not have as clear an underlying structure as traditional databases do. We propose a graphical object-based model, called VideoGraph, in this paper. This scheme has the follo... | [
1100,
2540
] | Train |
3,182 | 2 | Management of XML Documents in an Integrated Digital Library We describe a generalized toolset developed by the Perseus Project to manage XML documents in the context of a large, heterogeneous digital library. The system manages multiple DTDs through mappings from elements in the DTD to abstract document structures.... | [
2745
] | Test |
3,183 | 3 | Capacity-Augmenting Schema Changes on Object-Oriented Databases: Towards Increased Interoperability The realization of capacity-augmenting schema changes on a shared database while providing continued interoperability to active applications has been recognized as a hard open problem. A novel three-pronged process, call... | [
254
] | Validation |
3,184 | 3 | Agent-Based Digital Libraries: Decentralization and Coordination This paper describes agent-based systems and explains why digital libraries should be built with this type of architecture. The primary advantage of agent-based architecture is decentralization, which enables scaling, flexibility, and extensibility. The c... | [
2604
] | Train |
3,185 | 4 | Patterns as Tools for User Interface Design . Designing usable systems is difficult and designers need effective tools that are usable themselves. Effective design tools should be based on proven knowledge of design. Capturing knowledge about the successful design of usable systems is important for both novice and e... | [
621
] | Train |
0 | 2 | Title: The megaprior heuristic for discovering protein sequence patterns
Abstract: Several computer algorithms for discovering patterns in groups of protein sequences are in use that are based on fitting the parameters of a statistical model to a group of related sequences. These include hidden Markov model (HMM) alg... | [
8,
14,
258,
435,
544
] | Test |
1 | 5 | Title: Applications of machine learning: a medical follow up study
Abstract: This paper describes preliminary work that aims to apply some learning strategies to a medical follow-up study. An investigation of the application of three machine learning algorithms-1R, FOIL and InductH to identify risk factors that gover... | [
344
] | Train |
2 | 4 | Title: Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in
Abstract: In cellular telephone systems, an important problem is to dynamically allocate the communication resource (channels) so as to maximize service in a stochastic caller enviro... | [
410,
471,
552,
565
] | Train |
3 | 4 | Title: Planning and Acting in Partially Observable Stochastic Domains
Abstract: In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. We begin by introducing the theory of Markov decision processes (mdps) and partiall... | [
197,
463,
601
] | Test |
4 | 3 | Note: c Massachusetts Institute of Technology The thesis consists of the development of this Michael I. Jordan Title: Professor
Abstract: Graphical models enhance the representational power of probability models through qualitative characterization of their properties. This also leads to greater efficiency in terms... | [
170
] | Train |
5 | 3 | Title: Some Experiments with Real-time Decision Algorithms
Abstract: Real-time Decision algorithms are a class of incremental resource-bounded [Horvitz, 89] or anytime [Dean, 93] algorithms for evaluating influence diagrams. We present a test domain for real-time decision algorithms, and the results of experiments wi... | [
490,
2164
] | Train |
6 | 6 | Title: A Formal Framework for Speedup Learning from Problems and Solutions
Abstract: Speedup learning seeks to improve the computational efficiency of problem solving with experience. In this paper, we develop a formal framework for learning efficient problem solving from random problems and their solutions. We apply... | [
251,
490
] | Train |
7 | 2 | Title: Optimal Alignments in Linear Space using Automaton-derived Cost Functions (Extended Abstract) Submitted to CPM'96
Abstract: In a previous paper [SM95], we showed how finite automata could be used to define objective functions for assessing the quality of an alignment of two (or more) sequences. In this paper, ... | [
258
] | Test |
8 | 2 | Title: Meta-MEME: Motif-based Hidden Markov Models of Protein Families
Abstract: In a previous paper [SM95], we showed how finite automata could be used to define objective functions for assessing the quality of an alignment of two (or more) sequences. In this paper, we show some results of using such cost functions.... | [
0,
14,
258,
435,
751
] | Validation |
9 | 6 | Title: Online Learning versus O*ine Learning
Abstract: We present an off-line variant of the mistake-bound model of learning. Just like in the well studied on-line model, a learner in the offline model has to learn an unknown concept from a sequence of elements of the instance space on which he makes "guess and test"... | [
308,
453,
481,
761
] | Train |
10 | 2 | Title: GRKPACK: FITTING SMOOTHING SPLINE ANOVA MODELS FOR EXPONENTIAL FAMILIES
Abstract: Wahba, Wang, Gu, Klein and Klein (1995) introduced Smoothing Spline ANalysis of VAriance (SS ANOVA) method for data from exponential families. Based on RKPACK, which fits SS ANOVA models to Gaussian data, we introduce GRKPACK: a ... | [
192,
193,
280,
420,
519
] | Train |
11 | 1 | Title: Simple Genetic Programming for Supervised Learning Problems
Abstract: This paper presents an evolutionary approach to finding learning rules to several supervised tasks. In this approach potential solutions are represented as variable length mathematical LISP S-expressions. Thus, it is similar to Genetic Progr... | [
624,
659
] | Train |
12 | 3 | Title: Estimating Bayes Factors via Posterior Simulation with the Laplace-Metropolis Estimator
Abstract: The key quantity needed for Bayesian hypothesis testing and model selection is the marginal likelihood for a model, also known as the integrated likelihood, or the marginal probability of the data. In this paper w... | [
84,
155
] | Validation |
13 | 0 | Title: Unifying Empirical and Explanation-Based Learning by Modeling the Utility of Learned Knowledge
Abstract: The overfit problem in empirical learning and the utility problem in explanation-based learning describe a similar phenomenon: the degradation of performance due to an increase in the amount of learned know... | [
482,
578,
1234
] | Train |
14 | 2 | Title: Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,
Abstract: Hidden Markov Models (HMMs) are applied to the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. The... | [
0,
8,
31,
232,
242,
258,
268,
384,
393,
400,
435,
437,
443,
544,
613,
708,
736,
746,
751
] | Train |
15 | 2 | Title: Back Propagation is Sensitive to Initial Conditions
Abstract: This paper explores the effect of initial weight selection on feed-forward networks learning simple functions with the back-propagation technique. We first demonstrate, through the use of Monte Carlo techniques, that the magnitude of the initial con... | [
80,
129,
152,
234,
253,
254,
322,
399,
538,
701
] | Train |
16 | 4 | Title: Exploration in Active Learning
Abstract: This paper explores the effect of initial weight selection on feed-forward networks learning simple functions with the back-propagation technique. We first demonstrate, through the use of Monte Carlo techniques, that the magnitude of the initial condition vector (in wei... | [
466,
552,
566,
1697
] | Train |
17 | 2 | Title: A Neural Network Model of Memory Consolidation
Abstract: Some forms of memory rely temporarily on a system of brain structures located in the medial temporal lobe that includes the hippocampus. The recall of recent events is one task that relies crucially on the proper functioning of this system. As the event ... | [
146
] | Train |
18 | 2 | Title: Topography And Ocular Dominance: A Model Exploring Positive Correlations
Abstract: The map from eye to brain in vertebrates is topographic, i.e. neighbouring points in the eye map to neighbouring points in the brain. In addition, when two eyes innervate the same target structure, the two sets of fibres segrega... | [
127,
427,
745,
747,
866,
890,
1932
] | Train |
19 | 2 | Title: Validation of Average Error Rate Over Classifiers
Abstract: We examine methods to estimate the average and variance of test error rates over a set of classifiers. We begin with the process of drawing a classifier at random for each example. Given validation data, the average test error rate can be estimated as... | [
74,
571
] | Train |
20 | 6 | Title: 25 Learning in Hybrid Noise Environments Using Statistical Queries
Abstract: We consider formal models of learning from noisy data. Specifically, we focus on learning in the probability approximately correct model as defined by Valiant. Two of the most widely studied models of noise in this setting have been c... | [
25,
267,
334,
640,
732
] | Train |
21 | 4 | Title: Decision Tree Function Approximation in Reinforcement Learning
Abstract: We present a decision tree based approach to function approximation in reinforcement learning. We compare our approach with table lookup and a neural network function approximator on three problems: the well known mountain car and pole ba... | [
294,
438,
567,
1378
] | Test |
22 | 1 | Title: Discovering Complex Othello Strategies Through Evolutionary Neural Networks
Abstract: An approach to develop new game playing strategies based on artificial evolution of neural networks is presented. Evolution was directed to discover strategies in Othello against a random-moving opponent and later against an ... | [
129,
163,
191,
1790,
2257
] | Train |
23 | 3 | Title: Applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses
Abstract: Technical Report No. 670 December, 1997 | [
41,
759
] | Train |
24 | 4 | Title: The Role of Transfer in Learning (extended abstract)
Abstract: Technical Report No. 670 December, 1997 | [
39,
269
] | Test |
25 | 6 | Title: General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting
Abstract: We derive general bounds on the complexity of learning in the Statistical Query model and in the PAC model with classification noise. We do so by considering the problem of boosting the accuracy of weak l... | [
20,
456,
778,
1181,
1897
] | Test |
26 | 2 | Title: Neural Network Applicability: Classifying the Problem Space
Abstract: The tremendous current effort to propose neurally inspired methods of computation forces closer scrutiny of real world application potential of these models. This paper categorizes applications into classes and particularly discusses feature... | [
747,
1129,
2612
] | Test |
27 | 3 | Title: Formal Rules for Selecting Prior Distributions: A Review and Annotated Bibliography
Abstract: Subjectivism has become the dominant philosophical foundation for Bayesian inference. Yet, in practice, most Bayesian analyses are performed with so-called "noninfor-mative" priors, that is, priors constructed by some... | [
84,
532
] | Train |
28 | 2 | Title: A Delay Damage Model Selection Algorithm for NARX Neural Networks
Abstract: Recurrent neural networks have become popular models for system identification and time series prediction. NARX (Nonlinear AutoRegressive models with eXogenous inputs) neural network models are a popular subclass of recurrent networks ... | [
586,
611,
1606,
1718
] | Validation |
29 | 5 | Title: Stochastically Guided Disjunctive Version Space Learning
Abstract: This paper presents an incremental concept learning approach to identiflcation of concepts with high overall accuracy. The main idea is to address the concept overlap as a central problem when learning multiple descriptions. Many traditional in... | [
319,
382,
426,
429
] | Train |
30 | 0 | Title: Towards More Creative Case-Based Design Systems
Abstract: Case-based reasoning (CBR) has a great deal to offer in supporting creative design, particularly processes that rely heavily on previous design experience, such as framing the problem and evaluating design alternatives. However, most existing CBR system... | [
231,
285,
679,
1148,
2276
] | Train |
31 | 2 | Title: GIBBS-MARKOV MODELS
Abstract: In this paper we present a framework for building probabilistic automata parameterized by context-dependent probabilities. Gibbs distributions are used to model state transitions and output generation, and parameter estimation is carried out using an EM algorithm where the M-step ... | [
14,
250,
1116
] | Train |
32 | 0 | Title: Design by Interactive Exploration Using Memory-Based Techniques
Abstract: One of the characteristics of design is that designers rely extensively on past experience in order to create new designs. Because of this, memory-based techniques from artificial intelligence, which help store, organise, retrieve, and r... | [
679
] | Validation |
33 | 2 | Title: Learning Generative Models with the Up-Propagation Algorithm
Abstract: Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden variables using top-down connections. The inversion process is i... | [
250,
1591,
1701
] | Train |
34 | 4 | Title: Using a Case Base of Surfaces to Speed-Up Reinforcement Learning
Abstract: This paper demonstrates the exploitation of certain vision processing techniques to index into a case base of surfaces. The surfaces are the result of reinforcement learning and represent the optimum choice of actions to achieve some go... | [
35,
66,
559,
565,
566
] | Train |
35 | 4 | Title: A Teaching Strategy for Memory-Based Control
Abstract: Combining different machine learning algorithms in the same system can produce benefits above and beyond what either method could achieve alone. This paper demonstrates that genetic algorithms can be used in conjunction with lazy learning to solve examples... | [
34
] | Train |
36 | 2 | Title: Generative Models for Discovering Sparse Distributed Representations
Abstract: We describe a hierarchical, generative model that can be viewed as a non-linear generalization of factor analysis and can be implemented in a neural network. The model uses bottom-up, top-down and lateral connections to perform Baye... | [
257,
745,
1066,
1591,
1701,
1933,
1974,
2072,
2390
] | Train |
37 | 4 | Title: Hierarchical Evolution of Neural Networks
Abstract: In most applications of neuro-evolution, each individual in the population represents a complete neural network. Recent work on the SANE system, however, has demonstrated that evolving individual neurons often produces a more efficient genetic search. This pa... | [
563
] | Train |
38 | 1 | Title: HOW TO EVOLVE AUTONOMOUS ROBOTS: DIFFERENT APPROACHES IN EVOLUTIONARY ROBOTICS
Abstract: In most applications of neuro-evolution, each individual in the population represents a complete neural network. Recent work on the SANE system, however, has demonstrated that evolving individual neurons often produces a m... | [
219,
273,
372,
563,
1689
] | Validation |
39 | 4 | Title: Finding Structure in Reinforcement Learning
Abstract: Reinforcement learning addresses the problem of learning to select actions in order to maximize one's performance in unknown environments. To scale reinforcement learning to complex real-world tasks, such as typically studied in AI, one must ultimately be a... | [
24,
82
] | Train |
40 | 6 | Title: On-line Learning with Linear Loss Constraints
Abstract: We consider a generalization of the mistake-bound model (for learning f0; 1g-valued functions) in which the learner must satisfy a general constraint on the number M + of incorrect 1 predictions and the number M of incorrect 0 predictions. We describe a g... | [
761
] | Train |
41 | 3 | Title: Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
Abstract: A critical issue for users of Markov Chain Monte Carlo (MCMC) methods in applications is how to determine when it is safe to stop sampling and use the samples to estimate characteristics of the distribution of interest. Research i... | [
23,
94,
115,
352,
533,
725,
888,
889,
892,
1713,
1716,
1733,
1982,
2456
] | Train |
42 | 1 | Title: Evolutionary Module Acquisition
Abstract: A critical issue for users of Markov Chain Monte Carlo (MCMC) methods in applications is how to determine when it is safe to stop sampling and use the samples to estimate characteristics of the distribution of interest. Research into methods of computing theoretical co... | [
163,
188,
189,
793
] | Train |
43 | 2 | Title: Competitive Anti-Hebbian Learning of Invariants
Abstract: Although the detection of invariant structure in a given set of input patterns is vital to many recognition tasks, connectionist learning rules tend to focus on directions of high variance (principal components). The prediction paradigm is often used to... | [
576,
747
] | Train |
44 | 0 | Title: Competitive Anti-Hebbian Learning of Invariants
Abstract: Instance-based learning methods explicitly remember all the data that they receive. They usually have no training phase, and only at prediction time do they perform computation. Then, they take a query, search the database for similar datapoints and bui... | [
88,
686,
2428
] | Validation |
45 | 4 | Title: Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation
Abstract: Discrete Bayesian models have been used to model uncertainty for mobile-robot navigation, but the question of how actions should be chosen remains largely unexplored. This paper presents the optimal solution to the problem... | [
220,
490,
492,
1459
] | Train |
46 | 2 | Title: The Pandemonium System of Reflective Agents
Abstract: In IEEE Transactions on Neural Networks, 7(1):97-106, 1996 Also available as GMD report #794 | [
238,
301,
489,
867
] | Validation |
47 | 0 | Title: An Implementation and Experiment with the Nested Generalized Exemplars Algorithm
Abstract: This NRL NCARAI technical note (AIC-95-003) describes work with Salzberg's (1991) NGE. I recently implemented this algorithm and have run a few case studies. The purpose of this note is to publicize this implementation a... | [
87
] | Train |
48 | 3 | Title: Sampling from Multimodal Distributions Using Tempered Transitions
Abstract: Technical Report No. 9421, Department of Statistics, University of Toronto Abstract. I present a new Markov chain sampling method appropriate for distributions with isolated modes. Like the recently-developed method of "simulated tempe... | [
725,
1783,
2456,
2682
] | Train |
49 | 0 | Title: Abstract
Abstract: We describe an ongoing project to develop an adaptive training system (ATS) that dynamically models a students learning processes and can provide specialized tutoring adapted to a students knowledge state and learning style. The student modeling component of the ATS, ML-Modeler, uses machine... | [
581,
643
] | Validation |
50 | 0 | Title: Abstract
Abstract: Metacognition addresses the issues of knowledge about cognition and regulating cognition. We argue that the regulation process should be improved with growing experience. Therefore mental models are needed which facilitate the re-use of previous regulation processes. We will satisfy this req... | [
150,
581,
583,
643
] | Train |
51 | 3 | Title: An Alternative Markov Property for Chain Graphs
Abstract: Graphical Markov models use graphs, either undirected, directed, or mixed, to represent possible dependences among statistical variables. Applications of undirected graphs (UDGs) include models for spatial dependence and image analysis, while acyclic di... | [
645,
772,
2559
] | Validation |
52 | 6 | Title: Theory Revision in Fault Hierarchies
Abstract: The fault hierarchy representation is widely used in expert systems for the diagnosis of complex mechanical devices. On the assumption that an appropriate bias for a knowledge representation language is also an appropriate bias for learning in this domain, we have... | [
228,
430,
478,
2487,
2580
] | Train |
53 | 1 | Title: DISTRIBUTED GENETIC ALGORITHMS FOR PARTITIONING UNIFORM GRIDS
Abstract: The fault hierarchy representation is widely used in expert systems for the diagnosis of complex mechanical devices. On the assumption that an appropriate bias for a knowledge representation language is also an appropriate bias for learnin... | [
243,
803,
1439,
1563
] | Train |
54 | 6 | Title: A Competitive Approach to Game Learning
Abstract: Machine learning of game strategies has often depended on competitive methods that continually develop new strategies capable of defeating previous ones. We use a very inclusive definition of game and consider a framework within which a competitive algorithm ma... | [
523,
615,
712,
1687,
2334
] | Train |
55 | 1 | Title: A Comparison of Selection Schemes used in Genetic Algorithms
Abstract: TIK-Report Nr. 11, December 1995 Version 2 (2. Edition) | [
163,
361,
844,
1784,
1832,
1905
] | Train |
56 | 6 | Title: Self bounding learning algorithms
Abstract: Most of the work which attempts to give bounds on the generalization error of the hypothesis generated by a learning algorithm is based on methods from the theory of uniform convergence. These bounds are a-priori bounds that hold for any distribution of examples and ... | [
778,
967,
1027
] | Train |
57 | 4 | Title: Markov Decision Processes in Large State Spaces
Abstract: In this paper we propose a new framework for studying Markov decision processes (MDPs), based on ideas from statistical mechanics. The goal of learning in MDPs is to find a policy that yields the maximum expected return over time. In choosing policies, ... | [
306,
552,
565,
967,
1459
] | Train |
58 | 2 | Title: Neural Networks with Quadratic VC Dimension
Abstract: This paper shows that neural networks which use continuous activation functions have VC dimension at least as large as the square of the number of weights w. This result settles a long-standing open question, namely whether the well-known O(w log w) bound, ... | [
536,
990,
1891,
2495
] | Train |
59 | 2 | Title: SELF-ADAPTIVE NEURAL NETWORKS FOR BLIND SEPARATION OF SOURCES
Abstract: Novel on-line learning algorithms with self adaptive learning rates (parameters) for blind separation of signals are proposed. The main motivation for development of new learning rules is to improve convergence speed and to reduce cross-ta... | [
570,
576,
839,
872,
874,
1520,
1709
] | Validation |
60 | 4 | Title: The Efficient Learning of Multiple Task Sequences
Abstract: I present a modular network architecture and a learning algorithm based on incremental dynamic programming that allows a single learning agent to learn to solve multiple Markovian decision tasks (MDTs) with significant transfer of learning across the ... | [
252,
552,
562,
565,
688
] | Train |
61 | 0 | Title: Program Synthesis and Transformation Techniques for Simpuation, Optimization and Constraint Satisfaction Deductive Synthesis of Numerical
Abstract: Scientists and engineers face recurring problems of constructing, testing and modifying numerical simulation programs. The process of coding and revising such simula... | [
240
] | Train |
62 | 3 | Title: Context-Specific Independence in Bayesian Networks
Abstract: Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution, eases knowledge acquisition, and supports effective inf... | [
324,
332,
423,
945,
2425,
2474,
2566
] | Validation |
63 | 4 | Title: Machine Learning, Reinforcement Learning with Replacing Eligibility Traces
Abstract: The eligibility trace is one of the basic mechanisms used in reinforcement learning to handle delayed reward. In this paper we introduce a new kind of eligibility trace, the replacing trace, analyze it theoretically, and show... | [
153,
210,
739,
1546
] | Test |
64 | 0 | Title: Integrating Creativity and Reading: A Functional Approach
Abstract: Reading has been studied for decades by a variety of cognitive disciplines, yet no theories exist which sufficiently describe and explain how people accomplish the complete task of reading real-world texts. In particular, a type of knowledge i... | [
284,
289,
486,
583
] | Train |
65 | 1 | Title: Integrating Creativity and Reading: A Functional Approach
Abstract: dvitps ERROR: reno98b.dvi @ puccini.rutgers.edu Certain fonts that you requested in your dvi file could not be found on the system. In order to print your document, other fonts that are installed were substituted for these missing fonts. Below... | [
743,
744
] | Train |
66 | 0 | Title: (1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological
Abstract: Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating i... | [
34,
69,
149,
183,
215,
288,
1248,
1531,
1698,
2122,
2157,
2294,
2310,
2359,
2441,
2520
] | Train |
67 | 3 | Title: Updates and Counterfactuals
Abstract: We study the problem of combining updates |a special instance of theory change| and counterfactual conditionals in propositional knowledgebases. Intuitively, an update means that the world described by the knowledgebase has changed. This is opposed to revisions |another in... | [
729
] | Test |
68 | 2 | Title: DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997
Abstract: Many neural net learning algorithms aim at finding "simple" nets to explain training data. The expectation is: the "simpler" the networks, the better the generalization on tes... | [
522,
979,
1632,
1825,
1845,
1979,
2007
] | Train |
69 | 0 | Title: SaxEx a case-based reasoning system for generating expressive musical performances
Abstract: We have studied the problem of generating expressive musical performances in the context of tenor saxophone interpretations. We have done several recordings of a tenor sax playing different Jazz ballads with different ... | [
66
] | Train |
70 | 6 | Title: Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods
Abstract: One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease... | [
255,
931,
999,
1521,
1692,
1986
] | Train |
71 | 3 | Title: Supervised learning from incomplete data via an EM approach
Abstract: Real-world learning tasks may involve high-dimensional data sets with arbitrary patterns of missing data. In this paper we present a framework based on maximum likelihood density estimation for learning from such data sets. We use mixture mo... | [
74,
661,
677,
929,
1559,
1641,
1923,
2442
] | Train |
72 | 2 | Title: SCRIPT RECOGNITION WITH HIERARCHICAL FEATURE MAPS
Abstract: The hierarchical feature map system recognizes an input story as an instance of a particular script by classifying it at three levels: scripts, tracks and role bindings. The recognition taxonomy, i.e. the breakdown of each script into the tracks and r... | [
202,
745,
747,
771
] | Train |
73 | 4 | Title: LEARNING TO GENERATE ARTIFICIAL FOVEA TRAJECTORIES FOR TARGET DETECTION
Abstract: It is shown how `static' neural approaches to adaptive target detection can be replaced by a more efficient and more sequential alternative. The latter is inspired by the observation that biological systems employ sequential eye-... | [
747
] | Train |
74 | 3 | Title: Hierarchical Mixtures of Experts and the EM Algorithm
Abstract: We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (G... | [
19,
71,
154,
193,
252,
263,
310,
345,
377,
505,
511,
547,
604,
622,
661,
680,
787,
867,
871,
881,
906,
949,
975,
987,
1017,
1024,
1103,
1220,
1634,
1676,
1928,
2013,
2124,
2266,
2284,
2335,
2390,
2421,
2513,
2654
] | Validation |
75 | 0 | Title: A Memory Model for Case Retrieval by Activation Passing
Abstract: We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models ... | [
288,
1123,
1354,
1854,
1855,
2122,
2299
] | Train |
76 | 3 | Title: A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS
Abstract: The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with re... | [
131,
181,
250,
345,
392,
518,
661,
694,
869,
975,
1128,
1548,
1934,
2327,
2390,
2532
] | Train |
77 | 2 | Title: Synchronization and Desynchronization in a Network of Locally Coupled Wilson-Cowan Oscillators
Abstract: A network of Wilson-Cowan oscillators is constructed, and its emergent properties of synchronization and desynchronization are investigated by both computer simulation and formal analysis. The network is a ... | [
337
] | Train |
78 | 6 | Title: Probabilistic Networks: New Models and New Methods
Abstract: In this paper I describe the implementation of a probabilistic regression model in BUGS. BUGS is a program that carries out Bayesian inference on statistical problems using a simulation technique known as Gibbs sampling. It is possible to implement s... | [
157,
214,
469,
560,
766,
2681
] | Validation |
79 | 6 | Title: A hierarchical ensemble of decision trees applied to classifying data from a psychological experiment
Abstract: Classifying by hand complex data coming from psychology experiments can be a long and difficult task, because of the quantity of data to classify and the amount of training it may require. One way to... | [
438,
2207
] | Validation |
80 | 2 | Title: Neural Network Implementation in SAS R Software
Abstract: The estimation or training methods in the neural network literature are usually some simple form of gradient descent algorithm suitable for implementation in hardware using massively parallel computations. For ordinary computers that are not massively p... | [
15,
2044
] | Train |
81 | 3 | Title: A Modification to Evidential Probability
Abstract: Selecting the right reference class and the right interval when faced with conflicting candidates and no possibility of establishing subset style dominance has been a problem for Kyburg's Evidential Probability system. Various methods have been proposed by Lou... | [
647
] | Validation |
82 | 4 | Title: A Reinforcement Learning Approach to Job-shop Scheduling
Abstract: We apply reinforcement learning methods to learn domain-specific heuristics for job shop scheduling. A repair-based scheduler starts with a critical-path schedule and incrementally repairs constraint violations with the goal of finding a short ... | [
39,
239,
295,
305,
410,
548,
565,
1378,
1440,
1553
] | Train |
83 | 2 | Title: A Neural Network Pole Balancer that Learns and Operates on a Real Robot in Real Time
Abstract: A neural network approach to the classic inverted pendulum task is presented. This task is the task of keeping a rigid pole, hinged to a cart and free to fall in a plane, in a roughly vertical orientation by moving t... | [
294,
747
] | Validation |
84 | 3 | Title: Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models
Abstract: Technical Report no. 255 Department of Statistics, University of Washington August 1993; Revised March 1994 | [
12,
27,
155,
347,
715,
950,
998,
1240,
1241,
1347,
1550,
1803
] | Test |
85 | 4 | Title: Q-Learning with Hidden-Unit Restarting
Abstract: Platt's resource-allocation network (RAN) (Platt, 1991a, 1991b) is modified for a reinforcement-learning paradigm and to "restart" existing hidden units rather than adding new units. After restarting, units continue to learn via back-propagation. The resulting r... | [
294,
425,
465,
552,
565,
2368
] | Train |
86 | 5 | Title: THE EXPANDABLE SPLIT WINDOW PARADIGM FOR EXPLOITING FINE-GRAIN PARALLELISM
Abstract: We propose a new processing paradigm, called the Expandable Split Window (ESW) paradigm, for exploiting fine-grain parallelism. This paradigm considers a window of instructions (possibly having dependencies) as a single unit, ... | [
249,
373,
652,
735
] | Test |
87 | 5 | Title: A Hybrid Nearest-Neighbor and Nearest-Hyperrectangle Algorithm
Abstract: We propose a new processing paradigm, called the Expandable Split Window (ESW) paradigm, for exploiting fine-grain parallelism. This paradigm considers a window of instructions (possibly having dependencies) as a single unit, and exploits... | [
47,
383,
719,
2021,
2245
] | Train |
88 | 6 | Title: Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation
Abstract: Selecting a good model of a set of input points by cross validation is a computationally intensive process, especially if the number of possible models or the number of training points is high. Techniqu... | [
44,
116,
208,
225,
251,
371,
587,
682,
760,
762
] | Validation |
89 | 6 | Title: NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all
Abstract: In many learning problems, the learning system is presented with values for features that are actually irrelevant to the concept it is trying to learn. The FOCUS algorithm, due to Almuallim and Dietterich, perform... | [
430,
635,
638
] | Train |
90 | 2 | Title: The wake-sleep algorithm for unsupervised neural networks
Abstract: We describe an unsupervised learning algorithm for a multilayer network of stochastic neurons. Bottom-up recognition connections convert the input into representations in successive hidden layers and top-down generative connections reconstruct... | [
680
] | Validation |
91 | 2 | Title: IEEE Learning the Semantic Similarity of Reusable Software Components
Abstract: Properly structured software libraries are crucial for the success of software reuse. Specifically, the structure of the software library ought to reect the functional similarity of the stored software components in order to facili... | [
745,
747
] | Test |
92 | 4 | Title: Learning Analytically and Inductively
Abstract: Learning is a fundamental component of intelligence, and a key consideration in designing cognitive architectures such as Soar [ Laird et al., 1986 ] . This chapter considers the question of what constitutes an appropriate general-purpose learning mechanism. We a... | [
136,
479,
552,
565,
1259,
2438
] | Train |
93 | 3 | Title: Blocking Gibbs Sampling for Linkage Analysis in Large Pedigrees with Many Loops
Abstract: Learning is a fundamental component of intelligence, and a key consideration in designing cognitive architectures such as Soar [ Laird et al., 1986 ] . This chapter considers the question of what constitutes an appropriat... | [
725,
759
] | Test |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.