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4 | We present coevolutionary approach learning sequential decision rules appears number advantages noncoevolutionary approaches The coevolutionary approach encourages formation stable niches representing simpler subbehaviors The evolutionary direction subbehavior controlled independently providing alternative evolving com... |
0 | Although applicable wide array problems demonstrated good performance number difficult realworld tasks neural networks usually applied problems comprehensibility acquired concepts important The concept representations formed neural networks hard understand typically involve distributed nonlinear relationships encoded l... |
5 | This paper describes method improving comprehensibility accuracy generality reactive plans A reactive plan set reactive rules Our method involves two phases formulate explanations execution traces generate new reactive rules explanations Since explanation phase previously described primary focus paper rule generation p... |
0 | The Bayesian analysis neural networks difficult simple prior weights implies complex prior distribution functions In paper investigate use Gaussian process priors functions permit predictive Bayesian analysis fixed values hyperparameters carried exactly using matrix operations Two methods using optimization averaging v... |
5 | This paper presents selfimproving reactive control system autonomous robotic navigation The navigation module uses schemabased reactive control system perform navigation task The learning module combines casebased reasoning reinforcement learning continuously tune navigation system experience The casebased reasoning co... |
5 | Current approaches computational lexicology language technology knowledgebased competenceoriented try abstract away specific formalisms domains applications This results severe complexity acquisition reusability bottlenecks As alternative propose particular performanceoriented approach Natural Language Processing based... |
1 | Performing policy iteration dynamic programming require knowledge relative rather absolute measures utility actions Baird calls advantages actions states Nevertheless existing methods dynamic programming including Bairds compute form absolute utility function For smooth problems advantages satisfy two differential cons... |
4 | We present coevolutionary architecture solving decomposable problems apply evolution artificial neural networks Although work preliminary nature number advantages noncoevolutionary approaches The coevolutionary approach utilizes divideandconquer technique species representing simpler subtasks evolved separate instances... |
0 | It widely considered ultimate connectionist objective incorporate neural networks intelligent systems These systems intended possess varied repertoire functions enabling adaptable interaction nonstatic environment The first step direction develop various neural network algorithms models second step combine networks mod... |
5 | Realistic complex planning situations require mixedinitiative planning framework human automated planners interact mutually construct desired plan Ideally joint cooperation potential achieving better plans either human machine create alone Human planners often take casebased approach planning relying past experience pl... |
0 | An intelligent system capable adapting constantly changing environment It therefore ought capable learning perceptual interactions surroundings This requires certain amount plasticity structure Any attempt model perceptual capabilities living system matter construct synthetic system comparable abilities must therefore ... |
4 | The emergence generalist specialist behavior populations neural networks studied Energy extracting ability included property organism In artificial life simulations organisms living environment fitness score interpreted combination organisms behavior ability organism extract energy potential food sources distributed en... |
1 | This paper highlights phenomenon causes deductively learned knowledge harmful used problem solving The problem occurs deductive problem solvers encounter failure branch search tree The backtracking mechanism problem solvers force program traverse whole subtree thus visiting many nodes twice using deductively learned ru... |
2 | This paper reexamines problem parameter estimation Bayesian networks missing values hidden variables perspective recent work online learning We provide unified framework parameter estimation encompasses online learning model continuously adapted new data cases arrive traditional batch learning preaccumulated set sample... |
4 | This paper analyzes convergence properties canonical genetic algorithm CGA mutation crossover proportional reproduction applied static optimization problems It proved means homogeneous finite Markov chain analysis CGA never converge global optimum regardless initialization crossover operator objective function But vari... |
2 | We attempt recover unknown function noisy sampled data Using orthonormal bases compactly supported wavelets develop nonlinear method works wavelet domain simple nonlinear shrinkage empirical wavelet coefficients The shrinkage tuned nearly minimax member wide range Triebel Besovtype smoothness constraints asymptotically... |
3 | Irrelevant redundant features may reduce predictive accuracy comprehensibility induced concepts Most common Machine Learning approaches selecting good subset relevant features rely crossvalidation As alternative present application particular Minimum Description Length MDL measure task feature subset selection Using MD... |
0 | This paper introduces magnetic neural gas MNG algorithm extends unsupervised competitive learning class information improve positioning radial basis functions The basic idea MNG discover heterogeneous clusters ie clusters data different classes migrate additional neurons towards The discovery effected heterogeneity coe... |
4 | In paper explore use adaptive search technique genetic algorithms construct system GABIL continually learns refines concept classification rules interaction environment The performance system measured set concept learning problems compared performance two existing systems IDR C Preliminary results support despite minim... |
0 | Neuralnetwork ensembles shown accurate classification techniques Previous work shown effective ensemble consist networks highly correct ones make errors different parts input space well Most existing techniques however indirectly address problem creating set networks In paper present technique called Addemup uses genet... |
0 | This research sponsored part National Science Foundation award IRI Wright Laboratory Aeronautical Systems Center Air Force Materiel Command USAF Advanced Research Projects Agency ARPA grant number F The views conclusions contained document author interpreted necessarily representing official policies endorsements eithe... |
3 | In many learning problems learning system presented values features actually irrelevant concept trying learn The FOCUS algorithm due Almuallim Dietterich performs explicit search smallest possible input feature set S permits consistent mapping features S output feature The FOCUS algorithm also seen algorithm learning d... |
0 | This paper explores application Temporal Difference TD learning Sutton forecasting behavior dynamical systems realvalued outputs opposed gamelike situations The performance TD learning comparison standard supervised learning depends amount noise present data In paper use deterministic chaotic time series lownoise laser... |
5 | Design rationale record design activity alternatives available choices made reasons explanations proposed design intended work We describe representation called Functional Representation FR used represent devices functions arise causally functions components interconnections We propose FR provide basis capturing causal... |
4 | The dominant component computational burden solving n n trivial p r b l e w h evolutionary algorithms task measuring fitness individual generation evolving population The advent r p l r e c n f g u r b l e f e l programmable gate arrays FPGAs idea evolvable hardware opens possiblity e b n g individual evolving populati... |
2 | Certain causal models involving unmeasured variables induce independence constraints among observed variables imply nevertheless inequality constraints observed distribution This paper derives general formula inequality constraints induced instrumental variables exogenous variables directly affect variables With help f... |
0 | We describe evaluate multinetwork connectionist systems composed expert networks By preprocessing training data competitive learning network system automatically organizes process decomposition expert subtasks Using several different types challenging problem assess approach degree automatically generated experts reall... |
0 | This report supported part Navy Medical Research Development Command Office Naval Research Department Navy work unit ONRReimb The views expressed article authors reflect official policy position Department Navy Department Defense US Government Approved public release distribution unlimited |
5 | This paper presents Objectdirected Case Retrieval Nets memory model developed application CaseBased Reasoning task technical diagnosis The key idea store cases ie observed symptoms diagnoses network enhance network object model encoding knowledge devices application domain |
0 | The NPcomplete problem determining whether two disjoint point sets ndimensional real space R n separated two planes cast bilinear program minimizing scalar product two linear functions polyhedral set The bilinear program vertex solution processed iterative linear programming algorithm terminates finite number steps poi... |
1 | Reinforcement learning methods applied control problems objective optimizing value function time They used train single neural networks learn solutions whole tasks Jacobs Jordan shown set expert networks combined via gating network quickly learn tasks decomposed Even decomposition learned Inspired Boyans work modular n... |
3 | The fault hierarchy representation widely used expert systems diagnosis complex mechanical devices This paper describes theory revision algorithm revises fault hierarchies This task presents several challenges typical training instances missing feature values pattern missing features significant rather merely effect no... |
6 | Many algorithms parameters set user For machine learning algorithms parameter setting nontrivial task influence knowledge model returned algorithm Parameter values usually set approximately according characteristics target problem obtained different ways The usual way use background knowledge target problem perform tes... |
2 | This paper introduces novel enhancement learning Bayesian networks bias small highpredictiveaccuracy networks The new approach selects subset features maximizes predictive accuracy prior network learning phase We examine explicitly effects two aspects algorithm feature selection node ordering Our approach generates net... |
1 | A number reinforcement learning algorithms developed guaranteed converge optimal solution used lookup tables It shown however algorithms easily become unstable implemented directly general functionapproximation system sigmoidal multilayer perceptron radialbasisfunction system memorybased learning system even linear fun... |
2 | Inferences measurement error models sensitive modeling assumptions Specifically model incorrect estimates inconsistent To reduce sensitivity modeling assumptions yet still retain efficiency parametric inference propose use flexible parametric models accommodate departures standard parametric models We use mixtures norm... |
6 | This paper demonstrates capabilities Foidl inductive logic programming ILP system whose distinguishing characteristics ability produce firstorder decision lists use output completeness assumption substitute negative examples use intensional background knowledge The development Foidl originally motivated problem learnin... |
0 | Understanding highdimensional real world data usually requires learning structure data space The structure may contain highdimensional clusters related complex ways Methods merge clustering selforganizing maps designed aid visualization interpretation data However methods often fail capture critical structural properti... |
3 | An interesting classical result due Jackson allows polynomialtime learning function class DNF using membership queries Since practical learning situations access membership oracle unrealistic paper explores possibility quantum computation might allow learning algorithm DNF relies example queries A natural extension Fou... |
4 | This paper studies balance evolutionary design human expertise order best design situated autonomous agents learn specific tasks A genetic algorithm designs control circuits learn simple behaviors given control strategies simple behaviors genetic algorithm designs combinational circuit switches simple behaviors perform... |
1 | This paper demonstrates exploitation certain vision processing techniques index case base surfaces The surfaces result reinforcement learning represent optimum choice actions achieve goal anywhere state space This paper shows strong features occur interaction system environment detected early learning process Such feat... |
0 | We study multivariate smoothing spline estimate function several variables based ANOVA decomposition sums main effect functions one variable twofactor interaction functions two variables etc We derive Bayesian confidence intervals components decomposition demonstrate even multiple smoothing parameters efficiently compu... |
6 | This paper presents methodological point view first results interdisciplinary project scientific data mining We analyze data carcinogenicity chemicals derived carcinogenesis bioassay program longterm research study performed US National Institute Environmental Health Sciences The database contains detailed descriptions... |
0 | We report development highperformance system neural network signal processing applications We designed implemented vector microprocessor packaged attached processor conventional workstation We present performance comparisons commercial workstations neural network backpropagation training The SPERTII system demonstrates... |
2 | Markov chain Monte Carlo MCMC algorithms revolutionized Bayesian practice In simplest form ie parameters updated one time however often slow converge applied highdimensional statistical models A remedy problem block parameters groups updated simultaneously using either Gibbs MetropolisHastings step In paper construct s... |
0 | Selective suppression transmission feedback synapses learning proposed mechanism combining associative feedback selforganization feedforward synapses Experimental data demonstrates cholinergic suppression synaptic transmission layer I feedback synapses lack suppression layer IV feedforward synapses A network feature us... |
3 | ARCING THE EDGE Leo Breiman Technical Report Statistics Department University California Berkeley CA Abstract Recent work shown adaptively reweighting training set growing classifier using new weights combining classifiers constructed date significantly decrease generalization error Procedures type called arcing Breima... |
1 | Several researchers demonstrated neural networks trained compensate nonlinear signal distortion eg digital satellite communications systems These networks however require original signal distorted version known Therefore trained offline adapt changing channel characteristics In paper novel dual reinforcement learning a... |
6 | Topdown induction decision trees TDIDT popular machine learning technique Up till mainly used propositional learning seldomly relational learning inductive logic programming The main contribution paper introduction logical decision trees make possible use TDIDT inductive logic programming An implementation topdown indu... |
0 | A network WilsonCowan oscillators constructed emergent properties synchronization desynchronization investigated computer simulation formal analysis The network twodimensional matrix oscillator coupled neighbors We show analytically chain locally coupled oscillators piecewise linear approximation WilsonCowan oscillator... |
1 | MIT Media Laboratory Perceptual Computing Section Technical Report No Appeared th IEEE Intl Conference Pattern Recognition ICPR Vienna Austria Abstract We present foveated gesture recognition system guides active camera foveate salient features based reinforcement learning paradigm Using vision routines previously impl... |
3 | We consider problem learning functions fixed distribution An algorithm Kushilevitz Mansour learns boolean function f g n time polynomial L norm Fourier transform function We show KMalgorithm special case general class learning algorithms This achieved extending ideas using representations finite groups We introduce new... |
4 | Genetic algorithms GAs extensively used different domains means global optimization simple yet reliable manner They much better chance getting global optima gradient based methods usually converge local sub optima However GAs tendency getting moderately close optima small number iterations To get close optima GA needs ... |
5 | A major issue casebasedsystems retrieving appropriate cases memory solve given problem This implies case indexed appropriately stored memory A casebased system dynamic stores cases reuse needs learn indices new knowledge system designers envision knowledge Irrespective type indexing structural functional hierarchical o... |
5 | The relative performance different methods classifier learning varies across domains Some recent Instance Based Learning IBL methods IBMVDM use similarity measures based conditional class probabilities These probabilities key component Naive Bayes methods Given commonality approach interest consider differences two met... |
0 | Often learning data one attaches penalty term standard error term attempt prefer simple models prevent overfitting Current penalty terms neural networks however often take account weight interaction This critical drawback since effective number parameters network usually differs dramatically total number possible param... |
2 | In paper study new informationtheoretically justified approach missing data estimation multivariate categorical data The approach discussed modelbased imputation procedure relative model class ie functional form probability distribution complete data matrix case set multinomial models independence assumptions Based giv... |
4 | Genetic programming methodology program development consisting special form genetic algorithm capable handling parse trees representing programs successfully applied variety problems In paper new approach construction neural networks based genetic programming presented A linear chromosome combined graph representation ... |
3 | An XofN set containing one attributevalue pairs For given instance value corresponds number attributevalue pairs true In paper explore characteristics performance continuousvalued XofN attributes versus nominal XofN attributes constructive induction Nominal XofNs representationally powerful continuousvalued XofNs forme... |
0 | The main aim paper provide tutorial regression Gaussian processes We start Bayesian linear regression show change viewpoint one see method Gaussian process predictor based priors functions rather priors parameters This leads general discussion Gaussian processes section Section deals issues including hierarchical model... |
4 | Satisfiability SAT refers task finding truth assignment makes arbitrary boolean expression true This paper compares simulated annealing algorithm SASAT GSAT Selman et al greedy algorithm solving satisfiability problems GSAT solve problem instances extremely difficult traditional satisfiability algorithms Results sugges... |
1 | This paper presents Fringe Exploration technique efficient exploration partially observable domains The key idea applicable many exploration techniques keep statistics space possible shortterm memories instead agents current state space Experimental results partially observable maze difficult driving task visual routin... |
4 | Augmenting genetic algorithms local search heuristics promising approach solution combinatorial optimization problems In paper genetic local search approach quadratic assignment problem QAP presented New genetic operators realizing approach described performance tested various QAP instances containing facilitieslocatio... |
5 | This paper presents case study evaluating casebased system It describes evaluation Anapron system pronounces names combination rulebased casebased reasoning Three sets experiments run Anapron set exploratory measurements profile systems operation comparison Anapron namepronunciation systems set studies modified various... |
6 | A novel approach learning first order logic formulae positive negative examples incorporated system named ICL Inductive Constraint Logic In ICL examples viewed interpretations true false target theory whereas present inductive logic programming systems examples true false ground facts clauses Furthermore ICL uses claus... |
5 | Many casebased reasoning algorithms retrieve cases using derivative knearest neighbor kNN classifier whose similarity function sensitive irrelevant interacting noisy features Many proposed methods reducing sensitivity parameterize kNNs similarity function feature weights We focus methods automatically assign weight set... |
0 | In paper present framework building probabilistic automata parameterized contextdependent probabilities Gibbs distributions used model state transitions output generation parameter estimation carried using EM algorithm Mstep uses generalized iterative scaling procedure We discuss relations certain classes stochastic fe... |
4 | We used genetic programming evolve b h topology sizing numerical values component analog electrical circuit correctly classify incoming analog electrical signal three categories Then r e p e r r e f u r c e w dynamically changed adding new source run The p p e r e c r b e h w h e |
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