Above are papers related to the following paper: [Paper_ID] Paper #1128990. 	[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 Programming (GP) but it employs a fixed set of non-problem-specific functions to solve a variety of problems. In this paper three Monk's and parity problems are tested. The results indicate the usefulness of the encoding schema in discovering learning rules for supervised learning problems with the emphasis on hard learning problems. The problems and future research directions are discussed within the context of GP practices. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35. 	[Title] 4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley. [Abstract] To implement a specific application, you should only have to change the file app.c. Section 2 describes the routines in app.c in detail. If you use additional variables for your specific problem, the easiest method of making them available to other program units is to declare them in sga.h and external.h. However, take care that you do not redeclare existing variables. Two example applications files are included in the SGA-C distribution. The file app1.c performs the simple example problem included with the Pascal version; finding the maximum of x 10 , where x is an integer interpretation of a chromosome. A slightly more complex application is include in app2.c. This application illustrates two features that have been added to SGA-C. The first of these is the ithruj2int function, which converts bits i through j in a chromosome to an integer. The second new feature is the utility pointer that is associated with each population member. The example application interprets each chromosome as a set of concatenated integers in binary form. The lengths of these integer fields is determined by the user-specified value of field size, which is read in by the function app data(). The field size must be less than the smallest of the chromosome length and the length of an unsigned integer. An integer array for storing the interpreted form of each chromosome is dynamically allocated and assigned to the chromosome's utility pointer in app malloc(). The ithruj2int routine (see utility.c) is used to translate each chromosome into its associated vector. The fitness for each chromosome is simply the sum of the squares of these integers. This example application will function for any chromosome length. SGA-C is intended to be a simple program for first-time GA experimentation. It is not intended to be definitive in terms of its efficiency or the grace of its implementation. The authors are interested in the comments, criticisms, and bug reports from SGA-C users, so that the code can be refined for easier use in subsequent versions. Please email your comments to rob@galab2.mh.ua.edu, or write to TCGA: The authors gratefully acknowledge support provided by NASA under Grant NGT-50224 and support provided by the National Science Foundation under Grant CTS-8451610. We also thank Hillol Kargupta for donating his tournament selection implementation. Booker, L. B. (1982). Intelligent behavior as an adaptation to the task environment (Doctoral dissertation, Technical Report No. 243. Ann Arbor: University of Michigan, Logic of Computers Group). Dissertations Abstracts International, 43(2), 469B. (University Microfilms No. 8214966) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1119140. 	[Title] Truth-from-Trash Learning and the Mobot [Abstract] As natural resources become less abundant, we naturally become more interested in, and more adept at utilisation of waste materials. In doing this we are bringing to bear a ploy which is of key importance in learning | or so I argue in this paper. In the `Truth from Trash' model, learning is viewed as a process which uses environmental feedback to assemble fortuitous sensory predispositions (sensory `trash') into useful, information vehicles, i.e., `truthful' indicators of salient phenomena. The main aim will be to show how a computer implementation of the model has been used to enhance (through learning) the strategic abilities of a simulated, football playing mobot. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #103482. 	[Title] Trading Spaces: Computation, Representation and the Limits of Uninformed Learning [Abstract] fl Research on this paper was partly supported by a Senior Research Leave fellowship granted by the Joint Council (SERC/MRC/ESRC) Cognitive Science Human Computer Interaction Initiative to one of the authors (Clark). Thanks to the Initiative for that support. y The order of names is arbitrary. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152143. 	[Title] Is Transfer Inductive? [Abstract] Work is currently underway to devise learning methods which are better able to transfer knowledge from one task to another. The process of knowledge transfer is usually viewed as logically separate from the inductive procedures of ordinary learning. However, this paper argues that this `seperatist' view leads to a number of conceptual difficulties. It offers a task analysis which situates the transfer process inside a generalised inductive protocol. It argues that transfer should be viewed as a subprocess within induction and not as an independent procedure for transporting knowledge between learning trials. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114388. 	[Title] There is No Free Lunch but the Starter is Cheap: Generalisation from First Principles [Abstract] According to Wolpert's no-free-lunch (NFL) theorems [1, 2], gener-alisation in the absence of domain knowledge is necessarily a zero-sum enterprise. Good generalisation performance in one situation is always offset by bad performance in another. Wolpert notes that the theorems do not demonstrate that effective generalisation is a logical impossibility but merely that a learner's bias (or assumption set) is of key importance Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27510. 	[Title] Measuring the Difficulty of Specific Learning Problems [Abstract] Existing complexity measures from contemporary learning theory cannot be conveniently applied to specific learning problems (e.g., training sets). Moreover, they are typically non-generic, i.e., they necessitate making assumptions about the way in which the learner will operate. The lack of a satisfactory, generic complexity measure for learning problems poses difficulties for researchers in various areas; the present paper puts forward an idea which may help to alleviate these. It shows that supervised learning problems fall into two, generic, complexity classes only one of which is associated with computational tractability. By determining which class a particular problem belongs to, we can thus effectively evaluate its degree of generic difficulty. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110998. 	[Title] Interpretable Neural Networks with BP-SOM [Abstract] Interpretation of models induced by artificial neural networks is often a difficult task. In this paper we focus on a relatively novel neural network architecture and learning algorithm, bp-som, that offers possibilities to overcome this difficulty. It is shown that networks trained with bp-som show interesting regularities, in that hidden-unit activations become restricted to discrete values, and that the som part can be exploited for automatic rule extraction. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27514. 	[Title] Statistical Biases in Backpropagation Learning  Keywords: Cognitive Science, Pattern recognition [Abstract] The paper investigates the statistical effects which may need to be exploited in supervised learning. It notes that these effects can be classified according to their conditionality and their order and proposes that learning algorithms will typically have some form of bias towards particular classes of effect. It presents the results of an empirical study of the statistical bias of backpropagation. The study involved applying the algorithm to a wide range of learning problems using a variety of different internal architectures. The results of the study revealed that backpropagation has a very specific bias in the general direction of statistical rather than relational effects. The paper shows how the existence of this bias effectively constitutes a weakness in the algorithm's ability to discount noise. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #188318. 	[Title] Avoiding Overfitting with BP-SOM [Abstract] Overfitting is a well-known problem in the fields of symbolic and connectionist machine learning. It describes the deterioration of gen-eralisation performance of a trained model. In this paper, we investigate the ability of a novel artificial neural network, bp-som, to avoid overfitting. bp-som is a hybrid neural network which combines a multi-layered feed-forward network (mfn) with Kohonen's self-organising maps (soms). During training, supervised back-propagation learning and unsupervised som learning cooperate in finding adequate hidden-layer representations. We show that bp-som outperforms standard backpropagation, and also back-propagation with a weight decay when dealing with the problem of overfitting. In addition, we show that bp-som succeeds in preserving generalisation performance under hidden-unit pruning, where both other methods fail. Please summarise all the given information.

Paper #1128990 has the title `Simple Genetic Programming for Supervised Learning Problems  
` and the 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 Programming (GP) but it employs a fixed set of non-problem-specific functions to solve a variety of problems. In this paper three Monk's and parity problems are tested. The results indicate the usefulness of the encoding schema in discovering learning rules for supervised learning problems with the emphasis on hard learning problems. The problems and future research directions are discussed within the context of GP practices. 
`.
Question: Which category should Paper #1128990 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1128990 has the title `Simple Genetic Programming for Supervised Learning Problems  
` and the 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 Programming (GP) but it employs a fixed set of non-problem-specific functions to solve a variety of problems. In this paper three Monk's and parity problems are tested. The results indicate the usefulness of the encoding schema in discovering learning rules for supervised learning problems with the emphasis on hard learning problems. The problems and future research directions are discussed within the context of GP practices. 
`.
Question: Which category should Paper #1128990 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Genetic_Algorithms