Provided is a state determination apparatus that appropriately performs pattern classification processing and/or pattern determination processing even when a map generated by the SOM technique includes discontinuous image regions (e.g., split image regions). In the state determination apparatus, the matching processing unit obtains adaptability data indicating a correlation degree between template data indicating a predetermined state and the SOM output data. The state determination unit determines a state of an input data. This allows for appropriately performing pattern classification processing and/or pattern determination processing even when a map generated by the SOM technique includes discontinuous image regions (e.g., split image regions).
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A state determination apparatus comprising: feature vector obtaining circuitry configured to obtain feature vector data from measured data obtained by measuring an event with an unknown state; mapping conversion circuitry configured to obtain SOM output data by mapping the feature vector data into a space whose dimension differs from a dimension of the feature vector data; matching processing circuitry configured to obtain adaptability data indicating a correlation degree between template data indicating a predetermined state and the SOM output data; and state determination circuitry configured to determine a state indicated by the measured data based on the adaptability data. 2. The state determination apparatus according to claim 1, wherein the matching processing circuitry obtains M (M is a natural number) pieces of adaptability data D_f(1) to D_f(M), which respectively indicate the correlation degree with respect to M pieces of template data Tmpl(1) to Tmpl(M), and the state determination circuitry specifies the template data corresponding to the adaptability data whose correlation degree is the highest of the adaptability data D_f(1) to D_f(M), and determines that a state corresponding to the specified template data is the state indicated by the measured data. 3. The state determination apparatus according to claim 2, wherein the matching processing circuitry calculates inner products using M (M is a natural number) pieces of the template data and the SOM output data and obtains the calculated inner products as the adaptability data D_f(1) to D_f(M), and the state determination circuitry specifies the template data corresponding to the adaptability data whose correlation degree is the highest of the adaptability data D_f(1) to D_f(M) and determines that a state corresponding to the specified template data is the state indicated by the measured data. 4. The state determination apparatus according to claim 2, wherein the matching processing circuitry calculates Euclidean distances using M (M is a natural number) pieces of the template data and the SOM output data and obtains the calculated Euclidean distances as the adaptability data D_f(1) to D_f(M), and the state determination circuitry specifies the template data corresponding to the adaptability data whose correlation degree is the highest of the adaptability data D_f(1) to D_f(M) and determines that a state corresponding to the specified template data is the state indicated by the measured data. 5. A state determination apparatus comprising: feature vector obtaining circuitry configured to obtain feature vector data from measured data obtained by measuring an event with an unknown state; normalization circuitry configured to obtain a norm of the feature vector data and obtain normalized feature vector data by normalizing the feature vector data; mapping conversion circuitry configured to obtain SOM output data by mapping the normalized feature vector data into a space whose dimension differs from a dimension of the normalized feature vector data; matching processing circuitry configured to obtain adaptability data indicating a correlation degree between template data indicating a predetermined state and the SOM output data; activity value obtaining circuitry configured to obtain an activity value indicating a probability that the norm obtained by the normalization circuitry corresponds to a norm of the feature vector data obtained in the predetermined state; and state determination circuitry configured to determine a state indicated by the measured data based on the adaptability data and the activity value. 6. The state determination apparatus according to claim 5, wherein the activity value obtaining circuitry obtains the activity value for a state k (k is a natural number satisfying 1≦k≦M, and M is a natural number) through processing corresponding to f  ( x ) = 1 2  πσ 2  exp ( - ( x - μ ) 2 2  σ 2 ) ( cl2 ) where μ is an average value of norms of the feature vector data obtained in the state k, σ is a standard deviation, x is a value of the norm of the feature vector data, and f(x) is a probability density function. 7. The state determination apparatus according to claim 5, wherein the matching processing circuitry obtains M (M is a natural number) pieces of adaptability data D_f(1) to D_f(M), which respectively indicate the correlation degree with respect to M pieces of template data Tmpl(1) to Tmpl(M), the activity value obtaining circuitry obtains activity values D_act(1) to D_act(M), which respectively corresponds to a first state to M-th state, and the state determination circuitry obtains a determination evaluation value D_est(k) for k-th state (k is a natural number satisfying 1≦k≦M, and M is a natural number) based on the adaptability data D_f(k) for the k-th state and the activity value D_act(k) for the k-th state, and determines a state indicated by the measured data based on the obtained determination evaluation value D_est(k) for the k-th state. 8. The state determination apparatus according to claim 7, wherein the matching processing circuitry calculates inner products using M (M is a natural number) pieces of the template data and the SOM output data and obtains the calculated inner products as the adaptability data D_f(1) to D_f(M), and the state determination circuitry obtains the determination evaluation value D_est(k) for the k-th state through processing corresponding to D_est(k)=h(D_f(k),D_act(k)) h(x,y): a function of variables x and y, and determines that a state corresponding to the largest determination evaluation value of the determination evaluation values D_est(1) to D_est(M) is the state indicated by the measured data. 9. The state determination apparatus according to claim 7, wherein the matching processing circuitry calculates Euclidean distances using M (M is a natural number) pieces of the template data and the SOM output data and obtains the calculated Euclidean distances as the adaptability data D_f(1) to D_f(M), and the state determination circuitry obtains the determination evaluation value D_est(k) for the k-th state through processing corresponding to D_est(k)=h(F1(D_f(k)),D_act(k)) where h(x,y) is a function of variables x and y, and F1(x) is a monotonically decreasing function with respect to x and the value of the function F1(x) is a positive value or zero, and determines that a state corresponding to the largest determination evaluation value of the determination evaluation values D_est(1) to D_est(M) is the state indicated by the measured data. 10. The state determination apparatus according to claim 1, wherein the template data is composed of two-dimensional elements, and a sum of all values of the two-dimensional elements is “1”. 11. The state determination apparatus according to claim 1, wherein the template data is composed of two-dimensional elements, the template data is created based on N pieces of SOM output data obtained during a period from timing t=1 to timing t=N (N is a natural number) in which an identical state continues, and assuming that the template data is composed of m×n (m and n are natural numbers) pieces of data, the template data is created by setting a value gij to element data at a second-dimensional coordinate (i, j) (i and j are natural numbers satisfying 1≦i≦m and 1≦j≦n) of the template, the value gij being obtained by g if = 1 N  ∑ t = 1 N   D ij  ( t ) ( cl6 ) where gij is element data at a second-dimensional coordinate (i, j) (i and j are natural numbers satisfying 1≦i≦m and 1≦j≦n) of the template data, and Dij(t) is element data at a second-dimensional coordinate (i, j) of the SOM output data at timing t. 12. The state determination apparatus according to claim 1, wherein the template data is composed of two-dimensional elements, the template data is created based on N pieces of SOM output data obtained during a period from timing t=1 to timing t=N (N is a natural number) in which an identical state continues, and assuming that (1) the template data is composed of m×n (m and n are natural numbers) pieces of data, (2) gij is element data at a second-dimensional coordinate (i, j) (i and j are natural numbers satisfying 1≦i≦m and 1≦j≦n) of the template data, and (3) Dij(t) is element data at a second-dimensional coordinate (i, j) of the SOM output data at timing t, and the template data is created by setting an average value obtained by calculating an average value of the element data Dij(t) satisfying Dij(t)≧Th1 to a value gij at a second-dimensional coordinate (i, j) of the template data. 13. The state determination apparatus according to claim 1, wherein the template data is composed of two-dimensional elements, the template data is created based on N pieces of SOM output data obtained during a period from timing t=1 to timing t=N (N is a natural number) in which an identical state continues, and assuming that (1) the template data is composed of m×n (m and n are natural numbers) pieces of data, (2) gij is element data at a second-dimensional coordinate (i, j) (i and j are natural numbers satisfying 1≦i≦m and 1≦j≦n) of the template data, and (3) Dij(t) is element data at a second-dimensional coordinate (i, j) of the SOM output data at timing t, and the template data is created by setting a value gij to element data at a second-dimensional coordinate (i, j) (i and j are natural numbers satisfying 1≦i≦m and 1≦j≦n) of the template, the value gij being obtained by calculating an average value of values Dij(t) of the SOM output data at the coordinate (i, j) whose corresponding variance value vij is obtained by AveD ij = 1 N  ∑ t = 1 N   D ij  ( t ) ( cl8  -  1 ) v ij = 1 N  ∑ t = 1 N   ( D ij  ( t ) - AveD ij ) 2 ( cl8  -  2 ) and satisfies vij≦Th2. 14. The state determination apparatus according to claim 1, wherein the template data is composed of two-dimensional elements, the template data is created based on N pieces of SOM output data obtained during a period from timing t=1 to timing t=N (N is a natural number) in which an identical state continues, and assuming that (1) the template data is composed of m×n (m and n are natural numbers) pieces of data, (2) gij is element data at a second-dimensional coordinate (i, j) (i and j are natural numbers satisfying 1≦i≦m and 1≦j≦n) of the template data, and (3) Dij(t) is element data at a second-dimensional coordinate (i, j) of the SOM output data at timing t, and the template data is created by setting a value gij to element data at a second-dimensional coordinate (i, j) (i and j are natural numbers satisfying 1≦i≦m and 1≦j≦n) of the template, the value gij being obtained by calculating an average value of values Dij(t) of the SOM output data at the coordinate (i, j) whose corresponding variance value vij is obtained by AveD ij = 1 N  ∑ t = 1 N   D ij  ( t ) ( cl9  -  1 ) v ij = 1 N  ∑ t = 1 N   ( D ij  ( t ) - AveD ij ) 2 ( cl9  -  2 ) and satisfies vij≦Th2, the values Dij(t) of the SOM output data each satisfying Dij(t)>Th3 where Th3 is a predetermined threshold. 15. The state determination apparatus according to claim 1, wherein the template data is composed of two-dimensional elements, the template data is created based on N pieces of SOM output data obtained during a period from timing t=1 to timing t=N (N is a natural number) in which an identical state continues, and assuming that (1) the template data is composed of m×n (m and n are natural numbers) pieces of data, (2) gij is element data at a second-dimensional coordinate (i, j) (i and j are natural numbers satisfying 1≦i≦m and 1≦j≦n) of the template data, (3) Dij(t) is element data at a second-dimensional coordinate (i, j) of the SOM output data at timing t, and (4) σij is a standard deviation of element data at the coordinate (i, j) obtained by AveD ij = 1 N  ∑ t = 1 N   D ij  ( t ) ( cl10  -  1 ) v ij = 1 N  ∑ t = 1 N   ( D ij  ( t ) - AveD ij ) 2   σ ij = sqrt  ( v ij ) ( cl10  -  2 ) sqrt(x): a function that returns the square root of x, the template data is created by (A) setting an average values of the SOM output data Dij(t) to the value gij when abs(Dij(t)−AveDij)≦n1×σij is satisfied and (B) setting zero to the value gij when abs(Dij(t)−AveDij)≦n1×σij is not satisfied.